the mean distance of each molecule to its closest neighbor in the dataset), which is strongly reduced in reinforced (0.25-0.35) generators for maximizing the A 2A R pKi compared to the pre-trained model (0.66). For the mean minimum distances to the respective fine-tuning dataset, generally no large increase or decrease in distance is observed after reinforcement learning, which suggest exploitation around the active compounds rather than exploration to completely new chemical space. This is expected, due to the applicability domain as optimization criteria. As the difference between replicates are small (SEM < 0.1 for fraction of valid & unique & applicable & novel), only the first replicate was used for the following analyses of the results. For each optimization scenario the fraction of valid molecules, the fraction of unique molecules, the fraction of applicable molecules (according to respective QSPR OPS applicability domains), the fraction of novel molecules (not contained in any of the training sets), the average synthetic accessibility score, mean internal (i.e. the mean distance of each molecule to its closest neighbor in the dataset) and external distance is given. For the reinforced models the standard error of the mean of the three replicates is stated between brackets.