Overall, the inclusion of HITL-AL leads to a notable improvement in the outcomes of molecule generation across a various range of metrics. In comparison to when no chemist feedback is queried, the generated high-scoring molecules exhibit a significantly higher likelihood of being active against the target protein, as observed in interactions with chemist 3 (ANOVA p-value: 2.10 -146 < 0.05). Additionally, molecules show improved properties, including better SA (the lower the SA score the easier to synthesize a molecule), as evidenced in interactions with chemists 1 (ANOVA p-value: 9.10 -91 < 0.05) and 2 (ANOVA p-value: 5.10 -56 < 0.05), who focused their feedback on prioritizing molecules based on their SA, as well as higher QED, as a result of interacting with chemist 3 (ANOVA p-value: 4.10 -94 < 0.05). Notably, enabling HITL-AL with different chemists during a goal-oriented molecule generation process leads to a better alignment between predicted and oracle scores for the top generated molecules (represented by lower mean absolute errors, ANOVA p-values of 5.10 -156 , 7.10 -70 and 1.10 -250 from interacting with each chemist respectively). An interesting future direction would be to use an ensemble of predictive models updated from different chemists to score the generated molecules. Additionally, it does not compromise the final novelty and uniqueness of the generated molecules. The observed decrease in internal diversity compared to no feedback is not deemed significant (ANOVA pvalues of 0.22, 0.98 and 0.81 from interacting with each chemist respectively). These results underscore the importance of incorporating informed chemist feedback to augment ML predictions when used to guide molecule generation processes, ensuring more grounded predictions and leading to more meaningful and practical generation results.