This smaller batch size did in-fact improve sample-efficiency, similar to AHC, in early stages of training, but then quickly underwent mode collapse as evidenced by a drop in validity and uniqueness (Figures and). Moreover, KL regularization did not rescue mode collapse in any case, and sometimes worsened performance, suggesting it is not a sufficient regularization method in this context. Interestingly, our re-implementation of BAR performed particularly poorly in most cases except for DRD2 activity (the case study in the original implementation ). We propose that the best agent memory in this method may actually inhibit learning without notable improvements in-between updating the 'best agent'; in effect having two 'regularizers' inhibiting learning. As a result, decreasing the 'best agent' update frequency (from 5 as originally implemented) may improve performance. Overall, AHC shows a sample-efficiency well beyond other RL strategies for all tasks of practical importance (i.e., excluding the heavy atom task).