When evaluating students, we often aim to evaluate their understanding of the method, not necessarily their ability to memorize answers. Commonly, the dataset used for the development of ML models is randomly split into the training (examples in class) and testing sets (questions in exams). When these sets are similar, students/models memorizing answers may just do as fine as students/models understanding the class material. To ensure understanding will be evaluated (and model will be generalizable), we assembled and carefully curated a dataset comprising approximately 13,000 small molecule pKa values, and used our recently reported strategy to reduce the overlap between training and testing sets (Section S-II). This strategy has specifically been designed to generate testing and training which are dissimilar.