Filtered Vector Search (FVS), which combines vector embedding similarity with structured metadata predicates, has emerged as a core requirement in RAG and production retrieval systems. ACORN-1, the representative In-filtering algorithm that reuses an existing HNSW index, substantially reduces latency at low selectivity but suffers connectivity instability below 5% selectivity and recall collapse below 1%. We propose RACORN-1, an in-place extension of ACORN-1 that resolves this collapse via (i) Adaptive Search Fallback (ASF) -- repurposing filter-failing nodes as transient bridges to detour around severed paths; bridge and two-hop candidate selection uses stride sampling for spatial diversity. While filter-first ACORN-family methods have a structural recall trade-off relative to distance-first HNSW, RACORN-1 improves the trade-off curve via ASF, minimizing recall loss while substantially reducing latency. Across three 1M-scale and one 40M-scale dataset, RACORN-1 delivers approximately 9-26x latency reduction over HNSW in the sweet spot (1%-0.3%), and recovers ACORN-1's recall collapse from 0.45-0.72 (1%) and 0.03-0.10 (0.3%) to 0.70-0.96 and 0.77-0.98 respectively. For the extreme-low-selectivity regime where linear scan can outperform graph search, we combine RACORN-1 with (ii) Adaptive Exact Fallback (AEF) in a variant RACORN-1+, achieving recall 1.00 with 20-75x speedup at 1M <=0.1% and 13x speedup at 40M 0.01%. Under a Negative Correlation evaluation (K-means clusters), where ACORN-1 collapses (recall 0.08-0.41), RACORN-1 maintains recall 0.80-0.98 with a 5-9x latency advantage over HNSW. Together, RACORN-1 and RACORN-1+ form an ACORN-1-compatible mechanism robust to both extreme-low-selectivity and adversarial query-filter correlation.

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