For the training set with 40,000 unique molecules and roughly 2.5 resonance structures per molecule, using RIGR instead of performing data augmentation leads to a 56% reduction in training time and a 42% improvement in maximum RAM usage while training. Table shows the exact data for these training cost metrics. Therefore, in addition to being consistent with our knowledge of chemistry, RIGR offers a much easier alternative to data augmentation, eliminating the need for additional resonance generation and data pruning, thereby saving time during both data processing and model training.