To prove that Augmented Memory can perform MPO, we formulate a case study to generate potential dopamine type 2 receptor (DRD2) inhibitors by explicitly optimizing molecular docking scores (Figure ). For accessibility and reproducibility, we use the open-source AutoDock Vina for docking. A well-known failure mode of docking algorithms is they reward lipophilic molecules, e.g., possessing many carbon atoms, which can be promiscuous binders . Bjerrum et al. consider this and enforced molecules to possess a molecular weight (MW) < 500 Da but this is insufficient in preventing exploitation of the docking algorithm as we show in Appendix E. Following Guo et al. , we design the MPO as follows: MW < 500 Da, maximize QED , and minimize the Vina docking score, for chemical plausibility. AutoDock Vina is a relatively expensive oracle and we impose a computational budget of 9,600 oracle calls, similar to the 10,000 oracle calls enforced in the PMO benchmark. We compare Augmented Memory, REINVENT , AHC , and BAR as the optimization algorithms. To mimic a real-world drug discovery pipeline that discards unpromising molecules, we pool the results from triplicate experiments with the following filter: MW < 500 Da, QED > 0.4 (the DRD2 drug molecule, risperidone, has a QED of 0.66), and Vina docking score < -9.4 (risperidone's score). Figure shows the docking scores distribution with the number of molecules passing the filter and the IntDiv1 38 score annotated. Firstly, experience replay improves all base algorithms, further reinforcing its importance. Secondly, all algorithms with the exception of Augmented Memory perform similarly. Compared to AHC with experience replay, which is the second most sample efficient algorithm, Augmented Memory generates over 2,000 more molecules with a better docking score than risperidone, with a small trade-off in diversity (IntDiv1 38 of 0.801). We emphasize that AHC with experience replay only generates 1,667 molecules passing the filter.