In this paper, we trained PCM models on bioactivity data for 13 SLC6 members across various organisms. Our results demonstrated that a target-stratified data split led to over-optimistic models, while the realistic split recommended by Martin et al. resulted in more conservative and robust models, reducing the risk of over-fitting. Furthermore, Leave One Target Out (LOTO) validation was investigated for the potential of PCM in identifying novel drug candidates for unexplored orphan targets.