Especially, CL is dependent on many different factors, such as the affinity for metabolizing enzymes, plasma protein binding and renal clearance. While it is difficult to compare to other published CL QSPR models directly due to varying datasets, and evaluation methods, R 2 values between 0.09 37 (difficult, structurally dissimilar set) and 0.82 have been reported, with most studies reporting values between 0.2 and 0.4 on. A well-defined applicability domain is essential for the reliable application of QSAR/QSPR models. This holds especially in this context, where multiple QSPR models were built on different datasets, which thus have different applicability domains. Here, the applicability domain was defined based on an implementation of the TOPKAT OPS. This method was shown to discriminate well between inliers and outliers for the current tasks through bootstrapping analysis where higher/lower performance was observed for the inliers and outliers compared to the complete test sets respectively. However, a disadvantage of including the applicability domain as an objective in reinforcement learning is that the ability of DrugEx to explore new chemical space is restricted. There are many different definitions and methods to determine the applicability domain of a QSPR model, using a different applicability domain may increase the coverage of the generative model. Another option could be to use proteochemometric or multi-task models to leverage data from other proteins/tasks. Even though a considerable fraction (> 0.4) of the generated molecules is within the applicability domain of all relevant QSPR models, this fraction does not increase during reinforcement learning in every scenario. This indicates that the generator model does not learn how to generate molecules within the applicability domain well. A reason for this might be the binary nature of the "applicability domain tasks", therefore, implementing the applicability domain as a continuous objective might be beneficial in the context of reinforcement learning. This could, for example, be achieved by using the Mahalanobis distance of the TOPKAT OPS directly, rather than an empirical cutoff, to improve feedback to DrugEx.