Typically, in current drug discovery pipelines, molecules that meet a specific target property profile (according to a target property predictor) are tested experimentally after each cycle . However, immediate experimental labeling via wet-lab assays is often infeasible due to the significant time and monetary costs associated with synthesizing the compounds proposed by the generative model. Consequently, experimental labeling tends to be performed in batches rather than continuously. Moreover, previous studies have shown that the generated molecules with high predicted probability of meeting the target property profile often include many false positives according to wet-lab assays and in-silico oracles . At this stage, the property predictor needs to be refined (or replaced by alternative scoring functions), potentially involving manual intervention from human experts to acquire additional experimental data for training.