Filtered Vector Search: State-of-the-art and Research Opportunities

The integration of vector search into databases, driven by advancements in embedding models, semantic search, and Retrieval-Augmented Generation (RAG), enables powerful combined querying of structured and unstructured data. This paper focuses on filtered vector search (FVS), a core operation where relational predicates restrict the dataset before or during the vector similarity search (top-k). While approximate near neighbor (ANN) indices are commonly used to accelerate vector search by trading latency for recall, the addition of filters complicates performance optimization and makes achieving stable, declarative recall guarantees challenging. Filters alter the effective dataset size and distribution, impacting the search effort required. We discuss the primary FVS execution strategies – pre-filtering, post-filtering, and inline-filtering – whose efficiencies depend on factors like filter selectivity, cardinality, and data correlation. We review existing approaches that modify index structures and search algorithms (e.g., iterative post-filtering, filter-aware index traversal) to enhance FVS performance. This tutorial provides a comprehensive overview of filtered vector search, discussing its use cases, classifying current solutions and their trade-offs, and highlighting crucial research challenges and future directions for developing efficient and accurate FVS systems.