The CG models were first benchmarked at the same thermodynamic state they were parameterized at for their ability to reproduce on-target and off-target properties from the underlying atomistic simulations (Fig. ). Errors are only reported for the high and low resolution butanol mappings, R3 and R5, respectively, and CG PEG, as these capture the key mapping dependencies (results for the R2 and R4 mappings are discussed in the SI). Although specific pair-wise comparisons reveal distinct behaviors of these models (vide infra), the most salient observation is the surprisingly similar performance of all models. All models show consistently low RDF errors and high force errors, a pressure correction is critical for both SP and ML models, and all models overestimate diffusivity and underestimate H v as the mapping resolution decreases. This similar performance is the first indication that the additional complexity of the ML model is largely unused. Force errors are very similar across all models for all mappings (Fig. and). Although the SP models do not have any direct force information they reproduce force similarly to the TL and ML models. On close inspection (Fig. ), the SP models do have a nominally higher error due to the exclusion of many-body effects, but the difference is negligible compared with the absolute error and may reflect overfitting to frictional forces rather than a reduction in reducible errors. A similar observation was reported in our previous work, where IBI and force-matching produced forces with similar accuracy across all mappings despite only the latter being directly parameterized to the forces. The comparably high force errors in the ML model suggests that the irreducible error dominates the representability related errors for the forces at these CG resolutions. In particular, the ML model has sufficient complexity to describe many-body and anisotropic interactions; nevertheless, this additionally complexity does not result in lower force errors. Put differently, the comparable errors of the simple IBI models indicates that the structural information captures the mean forces equally well compared with supplying the force information directly. Some minor differences among the models can be observed for RDF, despite their overall similarly low errors (note the y-axis, Fig. ). The ML and TL models perform better for butanol than PEG, owing to the outsized role of the hydroxyl bead in determining the errors of butanol. In particular, these models reproduce intramolecular structural correlations better than the IBI models due to the inclusion of many-body interactions (Fig. ). However, the IBI-based models always outperform the ML model in capturing the intermolecular structural distributions (Fig. ). For the R5 mapping, where hydroxyl intramolecular correlations are no longer explicit, all models reproduce the RDF equally well (Fig. ). We will defer discussion of the ML simulations of PEG to the next section. Here we only note that the SP models outperform the TL models and transfer learning hurts the reproduction of PEG structure (Fig. ).