Using DF2 we re-ran the previous experiment on the four targets as before, shown in Figure . The change in DF stabilized learning over the full length of training while still resulting in similar optimization of docking score. Moreover, there was no convergence of normalized docking score to the minimum score threshold and thus uniqueness stayed relatively high (Figure ). To gain a quantitative understanding of improvement in sample-efficiency, Table compares the number of steps (and samples) required by AHC in combination with