Harmonizing Efficiency and Accuracy in Filtered Vector Search

Approximate nearest neighbor search (ANNS) is increasingly essential for AI-driven applications. In practice, ANNS is often coupled with label filtering to improve accuracy, relevance, and efficiency, a variant known as filtered ANNS or FANNS. Existing FANNS methods fall short in efficiency or accuracy. This is because, first, searching on a label-mixed index often converges to local minima due to label interference. Second, isolating and duplicating labels in the index inflates the index and incurs prohibitive memory overhead. Third, current join-based strategies for multi-filter search perform substantial wasted computation on unpromising candidates.

We present BigFANN, a label-aware FANNS framework that achieves high speed and space efficiency, while retaining high accuracy. Our framework features a hybrid indexing scheme that introduces heterogeneous edge types and flexible IVF-graph indexing. Specifically, we construct graph indices with tunable combinations of exclusive and shared edges based on label characteristics. This method effectively minimizes label interference for single-filter search under a certain memory budget, ensuring high search accuracy and speed. In addition to heterogeneous edges, we adopt a hybrid of graph and IVF indices to deal with single- and multi-filter queries. Particularly, for multi-filter searches, we propose a join-free search strategy to eliminate wasted computation in the existing join-based strategy. Experimental results on various datasets show that BigFANN significantly outperforms state-of-the-art FANNS frameworks, UNG and ParlayIVF 2 , by up to 786× and 4× respectively, while achieving the same or better accuracy.