Perhaps the single most parallelizable sampling protocol for free energy calculations use non-equilibrium methods to conduct the alchemical transformations (Fig. ). Briefly, these calculations compute the free energy differences (∆G) using a so-called "transformation" between the wild-type and mutant amino acid atoms. This transformation is "alchemical" in that it scales, using the parameter λ, bonded and nonbonded interactions of an amino acid sidechain found in wild-type (WT) and mutant such that λ = 0 represents a complete WT and λ = 1 represents the mutant; values of 0 < λ < 1 are a scaled linear combination of the two sets of interactions. During a transformation, the sidechain of one amino acid (WT) is "phased out" while another amino acid chain (the mutant) is "phased in" . A simulation transforms between the two amino acids by incrementally changing λ and computes the amount of thermodynamic work accumulated. Simulations cycle in both directions, transforming WT to mutant (forward) and mutant to WT (reverse), changing λ and tracking the amount of work accumulated, using an estimator to compute free energy difference (∆G) upon mutation (Fig. ) . Non-equilibrium cycling have already been run at scale to study mutations , and do screens of inhibitors Alternative methods can be run in parallel to generate massive datasets and scan across multiple transformations. Single-trajectory methods are able to be implemented to collect massive data in parallel . Expanded ensemble simulations are single independent trajectories that conduct the swaps in the single trajectory to maximize mixing. These methods show incredible promise at capturing the impact of mutations or screening multiple inhibitor binding affinities .