In the subsequent section, we present results derived from three distinct human experiments, each involving a different expert in fine-tuning a DRD2 bioactivity predictor within the multi-objective generation scenario described above. The two first experts are experienced in generative chemistry and interact regularly with synthetic chemists to suggest useful solutions and adapt the generative tools to their needs. The third expert is more experienced in medicinal chemistry. All are co-authors of the manuscript. We initiated our investigation by assessing the efficacy of our approach under various acquisition strategies-random, uncertainty, greedy, and EPIG-while querying a simulated expert. This expert's reliability was modeled with three noise levels: noise-free (ϵ = 0), moderate noise (σ ϵ = 1.0) and high noise (σ ϵ = 5.0). Our findings indicate that integrating AL to update the penalized LogP predictor contributes to the progressive alignment of penalized LogP estimates with oracle values over time (i.e., reduction of the MAE, figure ), resulting in the generation of molecules that are more likely to achieve the desired target (in this case, a penalized LogP within 2 and 4) according to the oracle (Figure ). Notably, these positive outcomes persist even in the presence of increased levels of noise in the simulated expert feedback, underscoring the robustness of our AL approach to unreliable inputs that may occur in real-world scenarios. We report these values for all generated molecules with a transformed predicted LogP score above 0.5. The generation runs with AL updates using EPIG as the acquisition criterion and noisy simulated experts are shown in red, while the "No Feedback" baseline is shown in gray.