RIGR is adapted from Chemprop's native featurizer by eliminating resonance-variant features such as bond order and formal charge, which results in treating all resonance structures of a molecule as identical. The motivation behind RIGR stems from a fundamental understanding of quantum chemistry, which abstracts the concept of chemical bonds and relies on a set of atomic coordinates and a guess geometry to solve for electronic densities that are used to determine contributions to molecular properties. On the other hand, ML approaches utilize graphs with additional atom and bond features that provide extra information to assist the model. However, the extra features are not strictly necessary for representing the underlying molecule and can make the representation inaccurate for molecules with resonance. In this paper, we provide a comprehensive comparison of the chemistry-abiding RIGR against Chemprop's native featurizer for predicting the standard heat of formation on a dataset primarily consisting of resonance-active molecules, including both radicals and closed-shell species. We evaluate and compare three types of models: those trained with the RIGR featurizer (RIGR), those trained with the native featurizer (Native), and those trained using the native featurizer on training set augmented with multiple resonance structures (Native + Aug). We discuss challenges related to data augmentation and introduce metrics that quantify the variance in property prediction across different resonance structures and its effect on model performance. We ultimately establish RIGR as a suitable featurizer that is applicable across a wide variety of property prediction tasks, both with and without resonance.