Methodological improvements in CLM training advanced the sampling of target-focused virtual molecule libraries. The hybrid CLM classifier successfully included both structural and bioactivity information of the fine-tuning molecules for the design of a virtual chemical library, thereby complementing the available methodological repertoire for virtual screening. It remains to be determined in more detail to what extent the CLM pretraining method affects model performance in the downstream task, i.e., molecular generation or ordinal classification. Importantly, CLM training was performed without data augmentation to study the positive effect of nucleus sampling on the generation of a SMILES string. Future improvement might be possible by combining nucleus sampling with data augmentation for CLM transfer learning . Given the setup of the present study, it was not possible to determine whether our hypothesis regarding the beneficial effect of model pretraining on patented chemical structures holds true. The long time required for hit-to-lead expansion and for preclinical and clinical drug development until a marketed drug is obtained will likely preclude any such analysis. Nonetheless, a PI3Kγ ligand with a new scaffold was computationally generated. Hit compound 1 was correctly predicted to be active with 80% confidence (80/100 votes). This result now motivates the synthesis of the top-ranking compounds with novel scaffolds. Obtaining rapid experimental validation of a set of readily available de novo designed molecules prior to embarking on de novo synthesis might help assess the value of computationally generated activity-focused chemical libraries. Future prospective studies will also have to assess the general applicability of this approach to other targets from different target families.