After reinforcement learning, the uniqueness decreases further for all optimization scenarios involving the A 2A R dataset. This dataset is characterized by lower chemical diversity than the PK dataset, which also limits the applicability domain of the QSPR model to a smaller part of the chemical space. The applicability domain thus confines the ability of DrugEx to explore new regions. Contrary to the valid and unique molecules, a higher fraction of novel (i.e. not presented in the fine-tuning dataset) molecules are generated by the reinforced generators than by the fine-tuned generators. This shows that the reinforced generator is not replicating the dataset molecules even though it generates quite similar molecules. The reduction in diversity is also confirmed by the mean minimum internal Tanimoto distance (i.e.