Drug design is a complex process that necessitates balancing cost, speed, and accuracy to achieve efficiency. In recent years, in silico methods have become increasingly important in enhancing these factors. A fundamental property in the multifaceted drug design process is potency, alongside ADME and synthetic accessibility. To evaluate the potency of potential drug candidates in silico, free energy calculation methods, including simulations on the theory level of molecular mechanics or quantum mechanics, are currently the state of the art that promise the most accurate estimates. Furthermore, large efforts are undertaken to automatize such approaches. In order to rank the most promising drug candidates by potency with in silico methods, ligands are typically ranked based on their calculated binding free energies. Binding free energy calculations can be categorized into two types depending on the calculated property: Relative Binding Free Energies (RBFE) or Absolute Binding Free Energies (ABFE). ABFEs provide direct insight into the binding free energy ∆G bind A of a ligand A but are computationally expensive as the entire ligand perturbation is simulated from a bound to an unbound state by turning all interactions of the ligand off and on again. In contrast, RBFEs leverage alchemical transformations of the ligands, representing a nonphysical transformation of a system between a ligand A, and a ligand B (end-states), enabling fewer atomic mutations leading to a smaller computational effort. The results describe the alchemical changes in water ∆∆G solv BA and in the complex ∆∆G complex BA with the target protein, which can be used to calculate the binding free energy difference between the two ligands, ∆∆G bind BA , required to rank the potential drug candidates (see Figure ). A crucial component of an RBFE calculation is the system representation. In previous work, Ries et al. identified three distinct ways of representing the system from literature: