Here, we take stock of the lessons learned in Ref. to critically assess two different probabilistic generative models (PGM) architectures used to train bijective maps between two metastable water ice crystal structures (Ice XI and Ic), modeled with a fully flexible empirical classical force field. In particular, we assess the efficiency and accuracy of the lattice-free energy estimates obtained by ML-enabled targeted free energy estimation against ground truth estimates obtained with an Einstein Crystal method. We examine the effect of different temperatures and system sizes on the convergence of free energy estimates using these mapping and estimation approaches and the quality of extrapolating these results to the thermodynamic limit. We note that the two temperatures examined (-150 and 50 o C) are meant to stress test the methods. For instance, at higher temperatures, we expect the configurational distributions to be significantly less localized and more anharmonic, making them more challenging to model using neural networks.