Human-in-the-loop (HITL) approaches were recently proposed to enhance the molecule generation process by allowing human experts to interact and provide feedback. For instance, these approaches enable the adaptation of scoring functions through RL with human feedback, ensuring that the generated molecules align better with desired properties . Based on empirical results showing the efficacy of a reward model trained on feedback from a chemistry expert regarding the optimization of DRD2 bioactivity , we posit that integrating feedback from well-aligned experts is crucial for ensuring that QSAR predicted scores which are optimized during the molecular generation process align well with the true scores of the target property.