Recent advances in the prediction of chemical reactivity and molecular properties using machine learning (ML) have sparked substantial interest in both academia and industry, particularly in pharmaceutical and material research and development, where rapid and accurate predictions of molecular properties can substantially accelerate the discovery and development of promising new drugs and materials. Furthermore, state-of-the-art deep learning architectures for chemical property prediction, especially directed message passing neural networks (D-MPNN) as exemplified by the open-source Chemprop 1 software package, have shown success in predicting a variety of chemical properties, including solvation thermodynamics, 2-5 regioselectivity, and reaction barriers. However, ML models tailored for molecular property and chemical reactivity prediction often rely heavily on extensive datasets to achieve some degree of generalization. This dependence poses a critical challenge in closed-loop generative molecular discovery campaigns, where achieving a robust generalization of property prediction models is crucial, but acquiring large-scale experimental data is often challenging and expensive. Therefore, developing strategies to improve the ML model performance, especially model robustness for novel molecules proposed by generative models, under significant data constraints is crucial in these domains.