Table : Performance of Augmented Memory, REINVENT , AHC , and BAR on the PMO benchmark . The mean and standard deviation of the AUC Top-10 is reported. The values obtained for REINVENT differ slightly from the PMO paper as we performed 10 independent runs compared to 5. Best performance is bolded and is relative to all models in the benchmark. * denotes superior performance to REINVENT but not overall, compared to other models in the benchmark. We note however, that we take the AUC Top-10 values for the other models as is from the PMO paper. If they were re-run with 10 different seeds (instead of 5), the values may decrease as was observed for REINVENT. The main motivation of our method is to improve sample efficiency. This would enable molecular generative models to explicitly optimize more expensive oracles which can afford increased predictive accuracy. We benchmark our method on the PMO benchmark proposed by Gao et al. which restricts the number of oracle calls to 10,000 and encompasses 23 tasks. The metric used is the Area Under the Curve (AUC) for the top 10 molecules. We note that Thomas et al. proposed a modified AUC Top-10 metric that incorporates diversity, but we omit comparison as the formulation can be subjective. The current Top AUC-10 metric assesses sample efficiency which is our focus. In the original PMO paper, REINVENT (with experience replay) is the most sample efficient model. We compare our method directly to REINVENT, BAR , and AHC which reports improved sample efficiency compared to REINVENT and is open-sourced. We also add experience replay to BAR and AHC to highlight its importance for sample efficiency. For a more statistically convincing comparison, we perform 10 independent runs (using 10 different seeds) compared to 5 used in the original PMO paper as the authors benchmarked 25 models, which imposed a significant computational cost. The optimal hyperparameters for REINVENT and AHC were used as provided in the PMO repository. We perform hyperparameter optimization for BAR following the PMO protocol (Appendix G) and Augmented Memory was run using REINVENT's optimal hyperparameters. The results show Augmented Memory significantly outperforms all methods and achieves superior performance to REINVENT across 19/23 tasks (14/23 compared to all models in PMO 11 ) (Table ). Moreover, the results reinforce the importance of experience replay as it improves the sample efficiency of both BAR and AHC, although neither outperform REINVENT. Finally, in the PMO paper , models were ranked based on the sum of the total AUC Top-10 and adjacently ranked models typically differ by 0.3-0.5. Augmented Memory outperforms REINVENT by 0.986 AUC Top-10 and yields a new state-of-the-art performance on the PMO benchmark.