Careful evaluation of NNPs is necessary when used outside their applicability domain Many NNPs lack inductive priors (domain-specific knowledge) and are primarily developed and evaluated against QM energies and forces, with little focus on their robustness in nanosecond MD simulations. Since most free energy methods rely on mixing the endstate Hamiltonians to sample at alchemical states, the mixed forces create another possible failure scenario. As a result, significant uncertainty is associated with the stability, robustness, and accuracy of NNPs in free energy calculations. Previous studies have demonstrated that while the ANI-2x NNP allows for linear interpolation between two Hamiltonians without singularities, the ANI-1ccx potential can lead to nonphysical water geometries, which lead to energy drifts. Similar failures have been observed for other NNPs outside their training domain. We investigate the robustness of ANI-2x for free energy calculations using independent free energy methods This study aims to answer several questions about NNPs and their use in free energy calculations. Specifically, we will perform MFES, uniand bidirectional FEP and NEQ switching simulations using ANI-2x as the target potential for a set of molecules in vacuum (see below for details). Previous studies have shown that converging FEP is challenging for some systems in this dataset. Comparing the free energy estimates from FEP and NEQ methods will provide insight into when NEQ methods are required. Having multiple independent methods estimating the same quantity will also provide confidence in the robustness of the NNP-based free energy protocols. We expect pathologies with the potential to become visible by comparing the estimates obtained with the different free energy approaches. Compared to QM calculations, the low computational cost of the ANI-2x NNP allows us to systematically investigate the effect of longer switching lengths on the variance of the work distributions. Additionally, we aim to investigate the systems identified in Schöller et al. as having non-Gaussian work distributions, exhibiting outliers or multimodality, by analyzing the torsion profiles and mapping the switching trajectories onto the torsional degrees of freedom.