Comparison with other approaches to improve model predictions. Our approach was rigorously compared with several baselines to assess its effectiveness. One baseline involved constraining the molecule generator to remain close to the training set, ensuring generated molecules were similar to known examples. Another baseline used transfer learning to pre-train the molecule generator using the initial training set of the predictor, enhancing the generator's focus on relevant chemical space from the start of the generation process. Among these approaches, constraining the generator with respect to training set similarity led to the lowest MAE between predicted and oracle values (Additional file 1: Figure ). While this strategy resulted in lower MAEs, it imposed strong limitations on the type of chemistry that could be explored. Moreover, pre-training the molecule generator significantly improved the LogP predictor generalizability in comparison with the "No Feedback" baseline, as well as AL using EPIG, resulting in a lower MAE at the start of the molecule generation process, which increased as the process continued. Notably, when using EPIG for AL, the MAE progressively reduced, eventually matching the MAE achieved by the pre-trained generator.