The ability to computationally predict kinetics quantities, such as rate constants of reactions involving biomacromolecules, remains an active pursuit in computational and theoretical biophysics. Many approaches rely on sampling possible reaction pathways using simulation methods such as molecular dynamics (MD) and Brownian dynamics (BD), however, the main challenge arises from the need to sample many MD simulation trajectories to obtain accurate predictions for important kinetics quantities, such as the koff. At present, the amount of brute force MD simulations required to obtain kinetics of ligand binding and unbinding remains intractable for most applications involving biologically relevant targets. Therefore, many clever approaches to avoid the cost of brute force MD simulations use a wide variety of schemes to expand the temporal and spatial reach available to the computational biophysics community to predict kinetics quantities. We, and others, have summarized these approaches elsewhere. SEEKR is one method we developed to utilize both MD and BD approaches such that we may exploit MD when explicit solvent and full molecular flexibility is required, but also exploit BD's speed when semi-rigid body molecules and implicit solvents will suffice. SEEKR accomplishes this by partitioning the phase space of a system into smaller regions, then simulating trajectories within these regions using whichever is the most appropriate simulation approach, allowing each region to be simulated in parallel. The question of how to determine the best partitions of the MD and BD regions is still not completely understood, although we surmise that the BD region should extend beyond the first, and probably second, solvation shell to *3 minimize inaccuracies caused by the implicit solvent. Within the solvation shells and the binding site itself, the explicit solvent and molecular flexibility of MD are likely required to obtain reasonable thermodynamic and kinetic quantities. In addition, by partitioning the phase space of molecular motion into smaller regions, one may ensure that events which are kinetically relevant but often rare, are adequately observed and characterized. The statistics obtained from short simulations in each of these smaller regions are then stitched together using milestoning theory. SEEKR performed well in predicting ligand-receptor kinetics, and was mostly successful in rank-ordering the affinity and residence times of a series of ligands binding to a receptor. The SEEKR approach was further augmented by utilizing a newer modification to milestoning theory, Markovian milestoning with Voronoi tessellations (MMVT). MMVT-SEEKR performed comparably well to the classical milestoning approach used in the earlier versions, with some added benefits, including an increase in accuracy for some quantities and decreased computational cost. All previous versions of SEEKR, including the MMVT version, used the NAMD simulation software package for MD. Here, we present SEEKR2, which uses the OpenMM simulation software suite for MD as an alternative to NAMD. OpenMM has enjoyed skyrocketing popularity among the scientific community because of its python interface, ease of extensibility, competitive performance on GPUs, and active development community. In addition, the design of OpenMM makes it relatively easy to develop independent plugins. Using SEEKR2, one may perform the MD portion of the SEEKR protocol in either OpenMM or NAMD, using either MMVT or the conventional milestoning method as developed by Elber and colleagues. A number of *4 auxiliary utility programs are also included to perform calculations using the typical SEEKR protocol.