Here, we propose pre-organizing ultra-large chemical libraries using hierarchical navigable small worlds and greedily traversing the underlying graph structure to find top-scoring molecules. This method recovers actionable virtual actives while screening a fraction of the chemical library and addresses As ultra-large libraries expand into the hundreds of billions, they will become too large to dock explicitly or comprehensively score by repeated ML prediction. However, any scoring function can guide HNSW structure traversal, so we expect RAD to complement hundred-billion-scale active learning workflows by vastly reducing the amount of chemical space to explore via rapid ML predictions. Across scales, RAD enables the discovery of high-scoring molecules from much larger libraries than would otherwise be accessible.