The Solute Carrier Transporter Family 6 (SLC6) represents an important group of transporters within the large family of Solute Carriers (SLCs), playing an essential role in transporting neurotransmitters, amino acids, and osmolytes across cell membranes. Comprising several subfamilies, SLC6 transporters are fundamental to an array of physiological processes and neural signaling pathways.(2) A prominent example is the Monoamine Transporter subfamily (MAT) comprising the serotonin-, norepinephrine-, and dopamine transporters, which have been a major area of interest for their therapeutic effects in treating neuropsychiatric disorders. Additionally, the SLC6 family includes other subfamilies, such as the GABA, amino acids, and nutrient transporter subfamilies.(2) However, these subfamilies are considerably less explored than the MAT subfamily. Proteochemometrics (PCM) modeling is a technique that builds on Quantitative Structure-Activity Relationship (QSAR) models by incorporating both protein and molecular information.(4) Previous work demonstrated several advantages of PCM modeling compared to traditional QSAR methods, such as providing more reliable predictions.(5-7) More importantly, PCM modeling has the potential to extrapolate chemical information across proteins. This is particularly useful to explore understudied protein members or even screen drugs against orphan targets.(4) PCM models are typically validated using both internal and external validation methods.(4) However, several factors influence the reliability of these validations, including how the dataset is split into training and testing sets and any imbalances in the dataset. For instance, a random split often leads to overoptimistic results, particularly when highly similar compounds are in both the training and test sets. Additionally, evaluating the entire test set with a single performance metric (like R 2 or MCC) can be skewed by imbalances in the data available for different targets. This can be highly misleading as it misrepresents the model's ability to extrapolate to new target spaces. These challenges highlight the importance of a careful and thoughtful evaluation strategy of PCM models. recently demonstrated the potential of PCM models, specifically for the SLC6 family, by identifying novel inhibitors for the norepinephrine transporter (SLC6A2). PCM modeling provided better performance compared to traditional QSAR methods, where the model was trained on the norepinephrine transporter data only. Given that the norepinephrine transporter is among the most studied SLC6 members for its clinical significance, our focus here will be on the understudied SLC6 members that lack sufficient data.