tween the core and its environment, such as hydrogen bonds or π-interactions, which are typical characteristics of biomolecular systems. For such systems, retaining an atomistic environment will perform better. This is the strategy used by quantum mechanics/molecular mechanics (QM/MM)-type embedding models as well as more advanced variants, such as the hybrid quantum mechanics/effective fragment potential (QM/EFP) model and the polarizable embedding (PE) model. One of the main limitations of classical atomistic models is the lack of exchange repulsion, which results in an inadequate description of short-range interactions. It manifests itself as electron spill-out and spurious molecular orbitals. To avoid these issues, quantum embedding models, which include exchange repulsion in some form, can be used. However, their applicability to biomolecular systems is limited by a comparably high computational cost; i.e., while such models are very efficient compared to conventional quantum chemistry methods, they are still too expensive compared to the requirements of, e.g., spectroscopic applications on large biomolecular systems, which easily consist of several tens or even hundreds of thousands of atoms and, moreover, require statistical sampling and thus evaluation of a large number of structures. There are thus limits on the size of systems that can be treated and the level of theory that can be used to describe the environment. The main bottleneck stems from the fact that either a quantummechanics-based calculation on the full system is required or, when relying on fragmentation procedures to split the environment into smaller fragments, an iterative procedure is needed in order to include environment polarization. The latter iterative procedure involves successive quantum-mechanics-based calculations on the individual fragments (often referred to as freeze-and-thaw cycles), and, while it is not strictly required, it is often important in order to obtain good accuracy.