We introduce an AL loop following each generation cycle where a data acquisition criterion is employed to select L batches of T predicted high-scoring molecules from a pool U r , aiming to enhance the property predictor f θ towards a specified objective. For instance, the EPIG acquisition criterion selects molecules whose observation by the predictor could reduce its predictive uncertainty at given inputs of interest (Equation ). For l = 1, . . . , L, each selected batch S l undergoes evaluation by a human expert to ensure relevance of high-scoring molecules with respect to the target property. To streamline human interaction and optimize sample efficiency, a small batch size T is utilized.