Free energy calculations are widely used in structure-based drug discovery Free energy calculations play a crucial role in understanding the thermodynamic properties of chemical and physical systems and have wide-ranging applications in fields such as molecular design, materials science, and biochemistry. The free energy difference between two states determines their relative stability and can be used to predict the direction and equilibrium of chemical reactions and biochemically relevant processes. Alchemical free energy simulations (FES) are widely used for calculating these differences, particularly in the context of binding, solvation, and partitioning; they are quickly becoming a standard method in computational chemistry. Neural network potentials offer increased accuracy compared to classical mechanics force fields The predictive power of FES depends on accurately describing the potential energy landscape of the system under study. In particular, the potential energy functions must adequately reproduce the intra-and intermolecular interactions. 2 When using classical force fields, failure to reproduce the correct potential energy surface of small molecules can be attributed to, among others, the limited functional form of the potential energy function. Class I molecular mechanics (MM) force fields (such as GAFF, CGenFF, 8 or OPLS ) and class II force fields typically omit higher-order terms, polarization effects, and essential couplings -such as torsion-torsion couplings -leading to significant deviations from quantum chemical energetics. The development of neural network potentials (NNPs) has been a significant advancement in computational chemistry. One of the most popular NNPs is the family of ANI potentials, 12,13 which has been used for various applications such as free energy calculations, conformer generation, 14 and chemical property prediction. Other examples of NNPs include PhysNet, SchNet, 17 NequIP, Allegro, and MACE. These NNPs can achieve accuracy comparable to high-level quantum mechanics (QM) calculations within the Born-Oppenheimer approximation. Once trained, these NNPs can evaluate energies and forces much more efficiently than QM calculations, making it possible to use them in applications such as FES that require sampling from the Boltzmann distribution using local methods like Markov chain Monte Carlo (MCMC) or molecular dynamics (MD). This is a significant advancement because it allows for more accurate potentials to model molecular energies/forces in cases where QM calculations were previously not practical due to their high computational cost. Indirect FES offers an elegant way to utilize multiple levels of theory While NNPs are significantly faster than QM calculations, they are still orders of magnitude slower than MM potentials. This can become limiting if configurations have to be sampled from multiple equilibrium Boltzmann distributions, as is the case for FES. Further, no established methodology exists combining NNPs and alchemical transformations, except for trivial cases (e.g., Wieder et al. ). This is analogous to hybrid QM / MM Hamiltonians, where several tricks frequently used in alchemical FES do not work. An alternative approach called indirect FES, or endstate correction, can avoid both difficulties. Indirect free energy calculations can be used to correct the results of an alchemical FES, performed with a cheap reference potential (typically an MM potential), with the accuracy of a target potential, typically a QM potential, by estimating the free energy difference between the reference and target potential ∆G MM→QM . For a relative solvation free energy ∆∆G A,B QM between molecules