The results, summarised at the top of Table , demonstrate the superior performance of GB-BI compared to previous generative models. Notably, the JANUS model performs well in these tasks due to its explicit use of molecular fragments. To further refine these tasks, we imposed a maximum limit of 2500 fitness function evaluations. The deep learning model within Tartarus uses ECFP4 fingerprints for molecular representation and was trained to predict optimisation values for specific tasks. For GB-BI and GB-EPI, we employed an archive of 150 niches, covering molecular masses from 200 u to 700 u, lipophilicity values from log P = -0.5 to log P = 5.5, topological polar surface areas (TPSA) from 0 Å 2 to 300 Å 2 , and Wildman-Crippen molar refractivity values between 0 and 300. No structural filters were applied.