Coarse-grained models often show poor performance at thermodynamic conditions other than they were parameterized at. Poor transferability may originate in representability limitations (e.g., the potential is incapable of accurately describing configurations with large density differences) or information limitations (e.g, the model didn't see configurations with large density differences during training and so poorly extrapolates). To test the performance of the models at distinct temperatures and densities, simulations were performed at 250 K and 350 K at atmospheric pressure (i.e., below and above the 298 K training temperature, respectively). We also tested the performance for a low density of 0.01 gm/cc to mimic gaseous state but found the CG models based on NNs (ML and TL) often failed due to the occurrence of configurations without the minimum number of neighbors required to generate an internal reference coordinate system for the DeePCG model (See SI). The normalized error in forces across all the models and systems at each temperature show comparably large values (Figure , note the axes). Given the large baseline magnitude in force errors we want to refrain from overinterpretting the apparent differences. We simply note that force errors marginally vary, and even reduce, when the models are used at alternative temperatures. Likewise, there is no clear transferability advantage presented by any of the models.