The enthalpy of vaporization (H v ) and molecular selfdiffusion coefficient (D) were used as off-target thermodynamic and dynamic properties that were unseen by the models during training. In previous work, we have found that H v is usually underestimated in CG models due to reduced attraction among beads in the condensed phase. Hence, pressure-corrected potentials, which incorporate additional attractive forces, better reproduce H v . This trend mainly persists here, with H v better reproduced by the ML model (besides the R4 and R5 mappings) and the pressure-corrected models T L PC , SP PC than SP and TL (Fig. and). In this context, the ML model does a good job on its own and transfer learning is not particularly advantageous. For the R5 mapping, the NN has no reference for the gas phase energy, which leads to the large error for the ML, TL, and T L PC models. This problem with the NN-based models can be easily resolved by adding an external reference. Another problem in calculation of H v was found for the R4 mapping, where it is not possible to simulate a vapor phase system because the DeePCG framework uses a local frame of reference that requires two neighbors, whereas the R4 mapping has only two beads. DeePCG could easily be augmented to correct this, it is merely discussed here to illustrate that the lack of a physical structure may unintentionally hurt the evaluation of some properties when using an ML model, and transfer learning alone does not correct this.