Our proposed approach is particularly relevant in contexts such as exploring novel protein receptors, where existing experimental data may be scarce, yet the need for finding new and potentially impactful ideas is pressing. By integrating expert feedback into the molecule generation process, we not only enhance the predictive capabilities of our proxy models but also ensure that the generated molecules are more meaningful and better aligned with the complexities of real-world scenarios. Furthermore, our methodology bridges the gap between AI-generated molecules and human intuition, fostering greater trust in the results outcomes. By incorporating expert feedback alongside existing experimental data, we create a closed-loop system wherein generative models continuously refine their predictions in response to real-world feedback. This iterative process leads to more reliable outcomes. As the field advances towards closed-loop discovery platforms, there is a growing trend of integrating automated iterative systems that cycle through hypothesis generation, experimentation and analysis. These platforms leverage AI to continuously generate and test new ideas, analyze results and refine models based on insights gained. Our approach represents a significant step forward in combining the strengths of AI and human expertise, paving the way for more robust processes that better serve realworld applications. However, it is crucial to acknowledge the inherent limitations of our method. The effectiveness of our approach relies heavily on the availability and expertise of human collaborators, introducing the potential for subjectivity and variability in feedback interpretation. Balancing these diverse perspectives while ensuring consistency in feedback poses a significant challenge that warrants careful consideration. In this work, we addressed this by downweighting uncertain human inputs during the fine-tuning of property predictors. Furthermore, the integration of human feedback into the generation process introduces computational complexities and overhead, potentially impacting scalability and efficiency, particularly in large-scale drug discovery projects. Ethical considerations surrounding data privacy, transparency, and bias mitigation are also paramount to ensure the responsible and equitable utilization of technology.