One of RAD's primary advantages over an active learning workflow is its lower computational overhead to prepare a screen, particularly against multiple protein targets. For a library of 100 million molecules, which is the approximate size of the D4 and AmpC libraries, the HNSW preparation time is approximately 20x faster than an active learning workflow's preparation (Figure ). Repeated rounds of ML model training dominate the active learning computation time at this dataset size and result in active learning taking longer than RAD. Scaling up, we estimated the approximate time for HNSW and active learning preparation for a dataset size of 4.8 billion -roughly the number of 3D molecules in ZINC-22. While ML predictions are faster than explicit docking, the sheer volume of predictions for 4.8