Neural search: Definition, how it works, benefits and more

Neural search is redefining the future of information retrieval, pushing beyond traditional keyword and vector search approaches. While vector search applies machine learning (ML) to map semantic relationships between user queries and data embeddings, neural search leverages deep neural networks (DNNs) to optimize the entire search pipeline, from indexing and semantic analysis to ranking mechanisms.

This advancement enables neural search systems to continuously learn and refine results, outperforming conventional search methods in accuracy and adaptability. More importantly, they support multimodal data retrieval, allowing seamless search across text, images, audio, and even complex 3D datasets, critical for AI-driven applications and next-gen user experiences.

However, adoption comes with challenges. Neural search models often function as ‘black boxes,’ raising concerns about interpretability, and their high computational requirements can drive up infrastructure costs.

This guide explains the fundamentals of neural search, how it differs from existing search systems, and its strategic advantages. It also explores practical applications and potential trade-offs, helping you assess whether neural search aligns with your company’s AI roadmap.

What is neural search?

Neural search, or neural information retrieval, leverages DNNs to interpret the contextual meaning behind search queries, enabling more accurate results than traditional keyword-matching systems and vector search. Instead of relying on exact terms, it transforms the user’s input and data into dense vector embeddings—numerical representations that capture semantic relationships. This process is similar to vector search systems, but instead of using ML models like approximate nearest neighbor (ANN) and a ranking algorithm, the neural search relies entirely on DNNs for the complete process.

A practical example involves querying a film database. A search like ‘Recommend a drama exploring modern parenting challenges’ would return a ranked list of movies aligned with the query’s intent, prioritizing contextual relevance over isolated keywords. The ranking is refined by the DNN itself, which needs to understand concepts like family dynamics and contemporary parenting struggles.

Here’s an example from Meilisearch:

You can also try this for yourself by checking out our demo.

A commonly misunderstood term is often confused with neural search. In the next chapter, we’ll look into the key distinctions between neural search and neural architecture search (NAS).

How does neural search differ from neural architecture search?

Neural search and NAS are often confused but serve different purposes. Neural search enhances information retrieval by understanding semantic meaning, while NAS focuses on automating the design of neural network architecture to optimize model performance for specific tasks.

NAS leverages techniques like recurrent neural networks and reinforcement learning (RL) to generate new language models, visual models, and overall neural architecture, eliminating manual experimentation and accelerating model development. In contrast, neural search applies deep learning to improve search relevance across text, images, sound, and other structured data types, transforming how systems retrieve and rank results.

Despite their differences, these technologies can intersect. For instance, you could use NAS to create a highly specialized artificial neural network tailored to your neural search. Let’s get a deeper understanding of how the latter works in the next chapter.

How does neural search work?

Neural search enhances the search experience with DNNs using two main mechanisms: indexing and embedding. These are integrated in the following steps:

• Data encoding and indexing: The DNN analyzes documents (e.g., movie descriptions) to generate vector embeddings. These dense numerical representations capture semantic contexts, such as themes, relationships, and emotions, and are properly indexed in a vector database.
• Query Encoding: The user’s input (e.g., ‘Recommend a drama about modern parenting’) is converted into an embedding using the same DNN. This ensures the query and data share a unified semantic framework.
• Semantic matching & ranking: Unlike vector search systems that rely on external algorithms (ANN, cosine similarity) the DNN directly compares query and document embeddings. It evaluates learned relationships (e.g., linking ‘parenting challenges’ to films about complex family dynamics) to rank results by contextual alignment.

Neural searches can process various data inputs, depending on their types and activation functions, including text, images, audio, and 3D information. The embedding model adapts based on the input type. For instance, a convolutional neural network (CNN) is used for images, while large language models like BERT, are employed for text.

What are some examples of neural search use?

Neural search can be applied to a wide range of use cases that require the processing of different types of data inputs and constant learning and optimization. Here are two simple examples of its potential applications:

1. Optimize ranking results: Jina AI offers a superior ranking solution based on neural search. It takes the initial outputs from a search query and reevaluates them to ensure they align more closely with the user's intent. Jina’s AI Reranker natural language processing model is available as open-source and can be accessed through Jina’s API.
2. Search tools with images: The German company PROFISHOP uses a search engine powered by CNNs, allowing users to submit an image as input and fetch related content from a large industrial, durable, and consumer goods database.
3. Retrieve ML models: Hugging Face has a large repository of models, including image-to-image, image-to-text, text-to-text, and more. Finding the appropriate model was initially challenging. However, the platform has since implemented a hybrid search system that combines keyword matching with contextual understanding to enhance model discovery.
4. Improve e-commerce: Google Shopping has integrated artificial intelligence (AI) to enhance its search capabilities, providing users with more relevant and personalized product recommendations. In addition, Google uses generative AI to summarize key factors during the search.

The neural search's ability to integrate RL for constant learning, accept different data types, and allow GenAI integration makes this technology extremely versatile and applicable to countless applications while ensuring accurate, personalized, and evolving results.

What are the benefits of neural search?

Neural search systems offer several key benefits. Let’s take a look at some of them:

• Increased engagement & reduced bounce rates: Users expect almost real-time, precise, and relevant search results. When they don’t find what they need, bounce rates surge, and potential customers may never return. An optimal bounce rate is usually below 50%.
• Enhanced productivity & efficiency: Instead of manually searching through vast documentation or using inefficient keyword searches, neural search enables employees to find exact information instantly.
• Multimodal search capabilities: Neural search processes various data formats, including text, images, and audio. This versatility allows new use cases, particularly in e-commerce. According to Think With Google, 50% of online shoppers say images directly influence purchasing decisions.
• Revenue growth: Search is an important yet undervalued feature in e-commerce. Bloomreach reports that while only 15% of B2C customers use site search, this small group drives 45% of online revenue.
• Personalization & continuous learning: Neural search systems can adapt by learning from user interactions and refining results based on behavior, clicks, and historical preferences. This dynamic personalization enhances user experience, improves retention, and drives long-term customer loyalty.

While the applications and benefits seem tempting, this is still a new field, so there is plenty of room for optimization. In the next section, we’ll explore some of this technology's challenges and limitations.

What are the drawbacks of neural search?

The key drawbacks of neural search are listed below:

• High computational cost and complexity: DNNs require substantial computational resources for training and operation. This led to the development of GPUs with enhanced parallel processing power, which can be expensive. For instance, the newest model from NVIDIA (which has a 90% share of the GPU market) is currently priced at $2,529.
• Data dependency: Neural search systems use high-quality, labeled training data to function properly. Without it, the system risks producing biased or skewed results. Designing the required metrics for the system before data ingestion is key to delivering better search functionality.
• Lack of interpretability: DNNs often function as 'black boxes,' learning complex patterns that can be difficult to understand. Due to this lack of transparency, debugging unexpected or odd results is challenging. This is why logs and monitoring tools are important once the search system is deployed.
• Model maintenance and engineering: To stay effective, DNNs require frequent retraining to adapt to language and user behavior changes, increasing operational overhead. Additionally, the infrastructure and expertise required to maintain neural systems can be too expensive for smaller organizations. AI engineers are among the most in-demand jobs according to LinkedIn’s 2025 report with an average annual salary of $120,000 globally, which is 30% higher than other engineering roles.

While technological advances are gradually reducing the computational demands of these models, these challenges must still be carefully considered when deciding whether to implement a neural search system.

How does neural search compare to other types of search?

Neural search is a form of semantic search, similar to vector search, but it uses DNNs to handle indexing, encoding, and ranking. Other search types include keyword search and hybrid search. Here’s how they differ:

Neural search vs. vector search

Both vector search and neural search retrieve data by matching query vectors to embeddings, making them forms of semantic search. Although neural search might be interpreted as a type of vector search, the latter typically refers to a method that leverages ML models like ANN for contextual retrieval and uses cosine similarity for ranking. In contrast, neural search bypasses these steps, relying entirely on DNNs throughout the search process.

Neural search vs. keyword search

Keyword search relies solely on matching literal terms in search queries to those in a dataset, ignoring context or meaning. For example, a keyword search for 'best smartphone' won’t return results mentioning 'top mobile devices' unless those exact words are present, whereas neural search recognizes their semantic equivalence.

Neural search vs. hybrid search

Hybrid search combines the contextual intelligence of DNNs with the speed and simplicity of keyword matching, optimizing for precision and efficiency. It uses keyword search to rapidly filter exact term matches while leveraging neural techniques for semantic understanding, ensuring relevance even when queries or data require deeper interpretation. Meilisearch gives you control over your hybrid search engine, enabling you to adjust the semantic context as needed.

What is the future of neural search?

The fast evolution of AI, specifically in DNNs and NAS, will bring tremendous advances to neural search and its use-case scenarios. NAS, which automates the design of DNN architecture, enables the creation of highly specialized models that optimize accuracy, speed, and resource efficiency for specific tasks. These new and more powerful models have transformative applications in different industries:

• Reshaping e-commerce: Neural search engines can retrieve the most relevant products based on the context provided by the customer’s query. Already, giants like Google are using this technology to enhance searches — Google Shopping now includes an AI-generated brief containing what it thinks are the top three choices and why. The AI brief also includes tips on what to look for when shopping for a particular product, which is sourced from the web and summarized by AI.
• Education & skill development: Learning platforms can use neural search to deliver materials tailored to an individual’s progress, learning style, and career goals. Imagine an AI tutor that dynamically assembles courses from videos, papers, and exercises. Three in five (60%) teachers claim to have integrated AI into their teaching habits.
• Healthcare transformation: Neural search engines can potentially analyze patient histories, symptoms, and genomic data to identify personalized treatment options or clinical trials. This could significantly reduce the time patients wait for an accurate diagnosis and treatment. Deep6 AI is an example of this, as it uses neural search techniques to sift through massive amounts of data to match patients to clinical trials.

Another emerging technology is Generative AI, which can go hand-in-hand with neural searches and be applied in different formats (text, image, sound, video):

• Design & creativity: Architects, for example, could use search engines by providing sketches and verbal descriptions (e.g., 'sustainable office building with natural light'), prompting the system to generate 3D models that integrate principles from existing designs and environmental data.
• Dynamic content creation: Media platforms could leverage generative AI to auto-generate videos, articles, or music playlists by retrieving and delivering content aligned with the user’s preferences, location, or mood.

These technologies will enhance information retrieval and actively influence human decision-making, creativity, and problem-solving as they evolve.

Unlock next-generation neural search with Meilisearch

Neural search systems are state-of-the-art in delivering contextual results, but they may not be the ideal choice for every situation. This is why a hybrid search system can be handy, especially when it offers control over the semantic ratio and ranking outcomes.