In this study, we simulate wet-lab experimental labeling and operate under the condition that ground truth values for a given target property k are consistently provided by an oracle f ⋆ (x). In our simulated environment, we have direct access to these oracle values for evaluating our models, rather than assuming their existence. To streamline the molecule generation process, we optimize a proxy f θ of the oracle f ⋆ to discover novel molecules. This proxy is typically derived through supervised learning from available data D 0 = {(x i , y i )} N0 i=1 , where x i ∈ R D represents a vector of D-dimensional count fingerprints for molecule i, and y i denotes its corresponding target value provided by the oracle f ⋆ (x i ). Specifically, y i = f ⋆ (x i ) ∈ R for regression tasks, and for classification tasks,