Understanding the binding energetics and interactions of inhibitors with their targets offers insight at the molecular level to facilitate drug design and discovery. Over 1000 high-resolution crystallographic structures of SARS-CoV-2 proteins have been determined, which has enabled structure-guided drug design efforts towards the development of potent compounds as therapeutic leads in the fight against COVID-19. In silico methods such as virtual screening, molecular dynamics, and binding free energy calculations have provided structural insight into the binding interactions, molecular mechanism, and conformational dynamics of inhibitors binding to the M pro target to inform COVID-19 drug discovery. In our own work, we have showed that alchemical absolute binding free energy (ABFE) calculations coupled with multiscale quantum chemical calculations can predict the binding energies of covalent inhibitors to the M pro target reasonably well. Building upon our previous work, we employ rigorous binding free energy calculations to explore the binding energetics of reversible covalent inhibitors of the SARS-CoV-2 M pro target. Reversible covalent inhibitors form covalent but reversible bonds with their target enzyme (Figure (a)) and are considered safer alternatives to conventional irreversible covalent inhibitors due to less risk for idiosyncratic toxicities. The inhibitors studied include 11a, 12 GC373, 14 MI-30, and PF-00835231 ('PF-231'), 17 . These inhibitors contain either an aldehyde or ketone functional group as electrophilic warhead and can reversibly bind to the catalytic Cys145 of the M pro , forming thiohemiacetal/thiohemiketal tetrahedral complexes. To calculate the absolute binding free energies of the inhibitors to the M pro target, we employed alchemical free energy calculations and ONIOM QM/MM calculations on model protein-ligand complexes of the system. The alchemical ABFE calculations were performed using both non-enhanced and enhanced replica-exchange sampling techniques within the equilibrium free energy framework. Our results indicate that enhanced free energy sampling methods via the Hamiltonian replica-exchange algorithm (H-REMD) improved the convergence and standard deviation of the binding free energy predictions. These results provide valuable insight into the binding energetics of reversible covalent inhibitors to the M pro target and highlights an important case where enhanced sampling techniques can improve the convergence of computed free energy estimates. This has the potential to assist in the rational design and evaluation of novel chemical entities, as well as support decisions in lead optimization campaigns towards identifying potent antiviral leads for current and future coronavirus diseases.