For high-fidelity calculations, the best choice appears to be τ 2B = 2.5 × 10 -4 E h , for both MBE(2) and MBE(3). The tighter value τ 2B = 1.25 × 10 -4 E h produces larger models, for which MBE(2) and MBE(3) results are actually marginally worse in a few cases, as judged by comparison to ∆E int computed using the full protein. This indicates that convergence of ∆E int need not be monotonic (to the full supramolecular result) with increasing model size, and that there is some interplay between the model size and the order of the n-body expansion. Larger models may introduce noise, stemming from finite-precision issues, while including less relevant residues that do not contribute meaningfully to the accuracy. Conversely, a smaller but well chosen model can focus on the most energetically significant interactions, leading to more accurate predictions for ∆E int at lower cost. In MBE(n) calculations, one should not assume that larger models are always more faithful to the full-system result, except possibly in very small models. As we refine these models, it is also crucial to consider how we evaluate their performance, particularly in terms of error reporting. Standard practice in fragmentbased quantum chemistry calculations is to report errors on a per-monomer basis. For applications of MBE(n) to water clusters, a target accuracy of 0.1 kcal/mol per monomer has been suggested, representing 10% of k B T at T = 298 K. The idea is that fragmentation errors of this magnitude are indistinguishable from thermal noise. Models with τ 2B = 2.5 × 10 -4 E h do achieve this level of accuracy, although the 0.1 × k B T standard is probably unnecessarily stringent for macromolecular ∆E int calculations. Even with the best conventional density functionals such as ωB97X-V, the disparity between singlepose ∆E int calculations (or even ensemble-averaged values, ∆E int ) and experimental binding affinities ∆G • bind is many times larger than 0.1k B T . (For a lengthy discussion of this point, see our recent work on fragmentation protocols for protein-ligand interaction energies. ) In addition, it is important to recognize the intrinsic limitations of semi-empirical quantum chemistry, as there is no sense in pushing for higher fidelity than is warranted by the intrinsic accuracy of the electronic structure method. To that end, we note that errors in HF-3c interaction energies average 4 kcal/mol for the combined L7 and S30L 75 data sets of large supramolecular complexes. This is comparable to the performance of dispersion-corrected and dispersion-inclusive DFT methods applied to the same data sets. One of the primary reasons to complete these calculations using fragmentation is the significant reduction in the cost per calculation. Given sufficient hardware, the wall-time cost of fragment-based calculations can be made very small because the subsystem calculations are inherently distributable. However, we are more interested in the extent to which the total (aggregate) computing time can be reduced via fragmentation. Aggregate computing time is a better metric for evaluating the cost because it reflects the carbon footprint of a given calculation, whereas wall time is a selfish time-to-solution metric. provides the same data for the supersystem HF-3c calculations. The latter were performed on a single compute node so they do not suffer from the low parallel efficiencies that typically characterizes massively parallel electronic structure calculations. Even so, the cost reduction is significant for the MBE(2) calculations, amounting to no more than 1-2% of the supersystem cost, depending on model size. For the largest system considered here (1BOZ, with 3,124 atoms), and for the model constructed using τ 2B = 2.5 × 10 -4 E h , the MBE(2) calculation requires 22 h or 0.4% of the conventional HF-3c cost, while MBE(3) requires 892 h or 18% of the cost without fragmentation. For 1O48 (with 1,781 atoms), the cost of the τ 2B = 2.5 × 10 -4 E h model is 2% of the supersystem cost for MBE(2) or 54% for MBE(3). Thus, fragmentation dramatically reduces the cost even for low-scaling methods like HF-3c that are already affordable in large systems. This presents a compelling advantage for highthroughput screening of different model-building algorithms, which is the topic of the next section.