Selective Memory Purge Enables Diverse Sampling while Retaining Efficiency. Figure demonstrates the enhanced sample efficiency of Augmented Memory but real-world applications of molecular generative models require the ability to sample diverse solutions. While aripiprazole is inherently an exploitation task, it can be framed as an exploration task if the goal is rephrased as: rediscover the target molecule and generate similar molecules. Using this formulation, we design experiments to prove that Augmented Memory can achieve diverse sampling. Figure shows the training plot across three methods: pure exploitation where diversity is not enforced, exploration using a diversity filter (DF) , and Selective Memory Purge. In the pure exploitation scenario, aripiprazole is rediscovered quickly (score of 1.0). In the DF experiment where a score of 0 is assigned for scaffolds sampled more than 25 times, mode collapse is observed (Figure ). By contrast, Selective Memory Purge maintains a moderate average score. The results from triplicate experiments were pooled to investigate the density of aripiprazole similarities (Figure ). As expected, in the pure exploitation scenario, most molecules are aripiprazole (Tanimoto similarity of 1.0). DF and Selective Memory Purge both enforce a wider distribution of similarities, but to varying degrees. In the shaded region (rediscovery score), Selective Memory Purge only shows a small density relative to DF. Moreover, Selective Memory Purge shows a flatter distribution of similarities. These observations demonstrate that Selective Memory Purge rediscovers the target molecule and enforces increased exploration compared to DF. To investigate this further, the Morgan fingerprints of the same pooled dataset were embedded using Uniform Manifold Approximation and Projection (UMAP) to visualize the chemical space. Qualitatively and quantitatively, Selective Memory Purge covers a larger chemical space (Figure ). The internal diversity (IntDiv1) metric was calculated as proposed in the MOSES benchmark , and measures the diversity within a set of generated molecules. Finally, we save the Agent states at every 5 epochs across the entire generative run and trace the NLL of sampling aripiprazole (Figure ). It is evident that Selective Memory Purge can discourage sampling of the target molecule more effectively than only using a DF. Importantly, the NLL also diverges, suggesting that the Agent is increasingly moving to chemical space dissimilar to aripiprazole as the generative experiment progresses.