Apart from the development of new reliable DFT functionals, many data-driven methods are actively developed to predict highly accurate molecular energies, which can be employed in large-scale explorative studies. Machine learning models based on graph neural networks, Gaussian Process Regression, and kernel ridge regression are some of the approaches that leverage modern computational architectures and big data to bypass the expensive DFT calculations. The general objective of such an approach is to predict the energies of stationary states in the potential energy surface. However, the performance of these models is often poor for new molecules that are different from training set molecules in terms of size or fragments (out-of-distribution systems). Thus, such data-driven models have limited usability for practical reaction network-building campaigns.