The expert baseline performed best in terms of DRD2 docking scores, with the fraction of DRD2+ (all) compounds reaching 45%. However, its SEDiv, filters, and presence scores decreased significantly, indicating catastrophic forgetting, a problem often encountered in transfer learning. This issue was particularly severe for synthesizability, with only 6.8% of the generated compounds present within the target chemical space. Consequently, the fraction of DRD2+ (in) compounds, which represents the efficiency in exploring the chemical space of interest, was no better than the prior or random baselines. be present within the target chemical space, as the PoE approach effectively mitigates inherent biases in the annotated dataset. This distinguishes the PoE chemical language model from existing fragment-based approaches, which cannot utilize external data sources and require the evaluation of individual building blocks or their combinations. In the Supporting Information, we confirm that the proposed approach can be successfully applied to a wide range of target proteins (Table -5).