To our knowledge, our BEBOP model is the first computational approach that starts from a minimum basis set Mulliken orbital population analysis of HF calculations and then uses those to construct total system energies (including approximate correlation and zero point energies implicitly) in terms of a variety of insightful chemical bonding and anti-bonding energy contributions. The net product is a "black-box" method that only requires the cost of HF theory while giving the accuracy of higher level quantum chemistry methods. In this way BEBOP relates to Miller and Manby's OrbNet Denali method that uses machine learning (ML) to map symmetry-adapted atomic orbital features from relatively low-cost semi-empirical quantum mechanics (SQM) theory to energies of high-level DFT theory calculations (i.e., ωB97X-D3/def2-TZVP). Key differences with the BEBOP approach are that much more robust (and much more computationally expensive) HF orbitals are used as a starting point, these are mapped to more accurate energies from higher-level ab initio thermochemical methods, and the system energies are partitioned into useful chemical bonding quantities that go beyond what is done in NBO and energy decomposition analysis (EDA) methods. At the present time, we only recommend its use for studies of thermochemical analyses of molecules whose geometries have been optimized to stationary points from other computational methods. As explained below, the BEBOP method uses a parametrization scheme that requires fewer parameters than most SQM and reactive forcefield methods and thus indicates promise for eventual transferability across large parts of the periodic table. There are also clear deficiencies in the present first-generation model that can be improved upon, though we note that many hybrid density functional methods also suffer from similar deficiencies and still have been widely adopted.