In this study, we have introduced an innovative approach that leverages humanmachine collaboration in the form of HITL-AL to fine-tune property predictors for molecule generation, thereby enhancing their generalizability and alignment with real-world applications. This methodology proves especially valuable in scenarios where limited labeled data is available for building generalizable proxies for molecule generation, and where pre-training the molecule generator or optimizing training data similarity might unduly constrain the diversity and creativity of the generated molecules.