More generally, our analyses show that while the errors in HOF computed by ab initio and tight-binding DFT methods tend to be larger in magnitude than for the NDDO-based semiempirical methods, the magnitude tends to scale systematically with the number and types of bonds in the molecule, which make them very amenable to improvement using the CBH approach. The NDDO-based semi-empirical methods are optimized by reducing the absolute HOF-error independent of system size and, as a result, the HOF-error of an individual molecule tends to be relatively random both in sign and magnitude, which makes them less amenable to improvement using the CBH approach. A better approach may be to parameterize the semiempirical methods by minimizing the CBH-corrected error and presenting the CBH-corrected energies to the user. For example, while NDDO-based methods would parameterize by minimizing the HOF-error of propane (∆HOF(propane)), a CBH-1-based scheme would minimize [∆HOF(propane)-(∆HOF(ethane)-∆HOF(methane)] and present the CBH-1 corrected HOF to the user. This approach might make it easier to find more generally applicable parameters that better and systematically minimize the error, since the underlying HF approach "naturally" gives larger errors for larger systems and we are no longer asking the parameters to "undo that" by searching for small errors independent of system size.