Rescored binary vector search in Neo4j: search more with less memory

Neo4j 2026.09 makes rescored binary vector search the default for newly created vector indexes across Aura, Enterprise Edition, and Community Edition, using 1-bit binary-quantized vectors for an expanded initial HNSW search before rescoring a limited candidate set with full-precision vectors stored on disk. The approach is intended to substantially reduce RAM requirements while preserving recall, and Neo4j reports benchmark improvements of roughly fivefold in both throughput and query latency compared with version 2026.08, with detailed methodology to follow. For 768-dimensional Float32 embeddings, the company estimates that a million-vector rescored binary index requires about 224 MB for its binary vectors and HNSW graph, compared with about 896 MB for scalar-quantized indexing, because the graph itself does not shrink proportionately with vector compression. Rescored binary is recommended for most workloads, while scalar quantization may provide up to 30% lower latency when sufficient memory is available, and users can adjust search expansion and fetch counts to prioritize recall. Existing indexes should be rebuilt after upgrading to obtain the newest improvements, and Aura users are advised to use vector-optimized configurations with ample disk capacity to retain the full-precision vectors needed for rescoring.

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