We next evaluate RIGR's potential as a general featurizer by testing it on a range of molecular property datasets that were used for benchmarking Chemprop. A summary of the datasets used for this analysis is provided in Table . For general usage, RIGR incorporates net charge as a molecule-level descriptor. The same training procedure and hyperparameter tuning, as implemented for benchmarking the performance of Chemprop v2, were applied to train models with RIGR to ensure a fair comparison. The results show a consistent per-formance between the models trained with RIGR and native featurizer across all property prediction benchmarks. Table and Table present some of the results from this benchmarking, highlighting the performance similarity between RIGR and Native. Other results are tabulated in Table , Table , and Table and the optimized hyperparameters for each task are listed in Table under Section S4.4 of the SI. All the results show that the models trained with RIGR exhibit performance comparable to those using the native featurizer.