We present a detailed comparison between models trained using RIGR and those trained with the native Chemprop featurizer, both with and without augmenting the data with resonance forms. While data augmentation was found to be useful, in many cases, generating a valid set of resonance structures is complicated and error-prone. RIGR offers a convenient and fast solution for ensuring resonance invariance and is demonstrated to have value as a general graph featurizer for wider applications, with robust performance on various property prediction tasks.