AL is an experimental strategy that involves iterative selection of new data with the goal of minimizing the number of necessary training data while maximizing the gain in predictive accuracy and expanding the applicability domain (i.e., the range of chemical space where the model can make reliable predictions) . An acquisition criterion is usually defined to select which experiments would contribute the most to an improved predictive accuracy. In the context of goal-oriented generation, AL can be used to encourage the generative agent to intentionally produce molecules that are poorly understood by the property predictor (e.g., by maximizing predictive uncertainty) which can then undergo experimental validation, serving as additional training data to enhance model generalization in subsequent generation cycles .