Protein-ligand (PL) interactions have drawn great amounts of scientific attention throughout the last century (see Refs. for a few recent textbooks and reviews). Besides being examined for playing crucial roles in a variety of essential biochemical processes, such interactions are often focused on in many drug design studies -revolving around finding inhibitors for proteins such as enzymes and neuroreceptors for the purpose of invoking a desirable biological response. Due to such considerations, many researchers from a broad spectrum of scientific disciplines (consisting of computational biologists and biochemists as well as theorists from chemistry and physics) have attempted to provide some general theoretical/computational modeling schemes for predicting biochemically-relevant PL binding events. It has been well-established that PL systems are greatly influenced by noncovalent interactions (NCIs). The latter, resulting from subtle electronic effects, are very small in magnitude and cannot virtually be measured by experimental means. Thus, ab initio electronic structure methods constitute a precious (and almost exclusive) source of information on biochemically-relevant NCIs -which, in turn, often used for the parametrization and calibration of more approximate computational modeling techniques (such as DFT functionals and molecular mechanics force fields). Ideally, one could use such nonempirical electronic structure methods for running molecular dynamics (MD) simulations on realistic PL systems; in such scenario, the information drawn from such simulations would include an adequate description of biochemically-significant NCIs, and it can thus be expected to offer desirable predictive power (which is, after all, the main goal of any theoretical model). However, electronic structure calculations are notorious for their steep computational cost scaling with the system's size -which generally precludes using them for MD simulations on realistically-sized biochemical systems (excluding a few recent approximate approaches, each entailing different methodological challenges; see, for instance, Refs. ).