To evaluate the ease of applying RAD to multiple proteins, we constructed a single HNSW for ~1 million ZINC "Goldilocks" molecules and used it to screen 43 DUDE-Z proteins. Performing this task with an active learning workflow would require progressively training and validating 43 individual ML models for each protein target and would take hundreds of hours. Furthermore, ML models predicting docking scores can require more than 50k-100k training examples per target, and may not converge effectively at this scale even with active learning. In contrast, constructing an HNSW for 1 million molecules takes only a minute or two and can be reused for each of the 43 proteins.