One of the key outcomes of this study is that it reveals the influence of the investigated chemical space of the molecules on the evaluation of the performance of the models. Although neither test set participated in the training process, the models performed relatively poorly in Test-2 (outside the training domain) whereas they did very well in Test-1 (within the training domain). It is important to note that, although the Test-2 data contains instances that have no overlap with the chemical space domain of the training set, it is still in relatively close proximity to this domain considering the vast chemical space of all organic compounds. This result shows that data-driven models not only tend to overfit on training data but also tend to overfit on their training domain. It points out that the ML models tested using only a test set sharing the same chemical space with the training set are very likely to fail on extrapolation. Therefore, it is crucial to have an additional test set that is not covered by the chemical space of the training set in order to determine the extrapolation capability of the proposed models.