The data generated for the bootstrapping analysis with the two different random splits was also used to analyze the ability of the applicability domain to discriminate inliers from outliers. The OPS was determined on each training set and subsequently used to split the test set into inliers and outliers. The performance of the models was calculated separately for the inliers (light green) and the outliers (peach) as shown in Figure . Here, we observe that according to the OPS applicability domain, there is a higher fraction of outliers in the cluster split test sets than for the random split. This corresponds well with the stricter nature of the cluster split which increases the chemical distance between the train and test set. In general, the performance of the model is worse (lower R 2 and higher RMSE) on the outliers than on the inliers, for both the cluster split and random split test sets. Furthermore, a reduction in the difference in performance between the random split and cluster split test sets can be observed for the inliers compared to the complete test set performance. Therefore, we concluded that the OPS applicability domain is sufficient to distinguish outliers during reinforcement learning.