In this study, we trained multiple Proteochemometrics (PCM) models using different data splits to investigate the potential for extrapolating chemical information through PCM. Our results show that splitting the data solely considering target stratification leads to over-optimistic models due to molecules experimentally tested on several targets, leading to identical molecules being present in both the training and test sets. Given that PCM focuses on targets within the same family with high sequence identity and similar function, molecules tested on one target are often evaluated on others. This results in many identical molecules being tested on multiple targets, leading to minimal chemical differences between the training and test sets if split randomly. Thus, a careful approach to data splitting is crucial to ensure the reliability and predictive accuracy of the model. Here, we implemented a realistic split that reduced the similarity between the training and test sets, which resulted in a more conservative model. We also explored the ability of PCM models to extrapolate chemical information to orphan targets using the Leave One Target Out (LOTO) split, with no success. This demonstrates a major limitation of PCM modeling. Finally, our models pointed the residue position A173 in hSERT corresponding to G149 and G153 in hNET and hDAT, respectively, to be relevant for subtype selectivity across the SLC6 family.