Figures S12-17 show the centroids of the largest clusters for the top 100 molecules generated during the six benchmark optimization tasks. Firstly, all strategies are more prone to generating unrealistic chemistry due to the broader training domain of the GuacaMol training set e.g., increasing molecular weight seen in the DRD2 docking score optimization task (Figure ). This is even observed for the more heavily regularized REINVENT strategy but is not present when using the MOSES n training set (Figure ). Moreover, KL regularization as proposed previously does not seem to improve chemistry generated by REINFORCE and HC and instead shows a tendency to increase molecular weight (Figure ). On the other hand, AHC results in chemistry similar to REINVENT and is typically more reasonable than REINVENT 2.0 (e.g., longer linker chains in Figure ), is less prone to idiosyncratic tendencies of HC (e.g., large molecules and long chains in Figure ), yet more sample-efficient than either. Overall, we believe AHC strikes the right balance in the trade-off between extrapolation and sample-efficiency due to effective, tunable regularization that can maintain training set properties and therefore the generation of sensible and realistic molecules de novo.