However, this is not guaranteed due to it being only a weak correlation. Moreover, we investigated the resulting scores of the in silico classification model on the real compounds in sub-groups α, β and γ, shown in Figure . Unsurprisingly, the α compounds are scored highest as they are also the training compounds representing the positive class. The compound scores for γ were higher than β in all projects except in-house projects D and F, suggesting that these models could guide RL to high activity compounds more frequently than low activity. Finally, we checked the learning curve for each target in Figure and found they could reach the higher scores close to 1.0. However, the representative scores for the γ sub-group were objectively lower (0.1 -0.8) than RL-achieved scores (usually 0.7 -1.0). This highlights that optimizing for high-scoring molecules is not necessarily going to generate molecules from the same distribution as the γ sub-group. In practice, this is not known apriori and exemplifies the difficulty in applying such routine QSAR models that may contain unexpected applicability domains, for example, predicting high-activity (γ sub-group) molecules with relatively low probabilities of activity. It is also worth noting that low predicted probabilities of activity do not necessarily translate to a negative prediction, as the optimal classification threshold may exist anywhere in the 0-1 range . Therefore, maximizing the binary label [0, 1] as a reward in RL based on an optimal decision threshold instead of the predicted probability may result in higher aSNN in these cases.