In this regard, we propose to involve human experts in the AL process, allowing them to re-evaluate (approve or refute) predicted scores for newly generated molecules and incorporate them as additional training data to refine the target property predictor. We apply the Expected Predictive Information Gain (EPIG) acquisition strategy which allows for prediction-oriented improvement, that is, favoring the acquisition of the most informative molecules to feed back to the property predictor to improve its predictive accuracy within specific regions of the chemical space (e.g., top-N ranked molecules). We empirically demonstrate the consistency of our HITL-AL approach in improving predictor's generalization with respect to the true target function even under noisy and uncertain expert feedback. This was shown first through simulations using noisy oracles as experts, then through interacting with human experts in chemistry using the Metis user interface , highlighting the value of domain expertise as a proxy for experimental labelling in AL settings.