WP6-G4 was made an optional system because guest G4 had silicon and present modeling difficulties with some tools. Nevertheless, 4 of 5 ranked submissions and an additional 4 nonranked submissions included these predictions. Predicted binding free energies had wide variability with DDG errors ranging from -3.58 to 3.02 kcal/mol (see Table †). MACHINE-LEARNING/NNET/DRAGON-descriptors was the only method to predict WP6-G4 within 1 kcal/mol of experiment (-7.77 ± 0.02 kcal/mol), with a slightly more favorable binding free energy as shown by a DDG error of -0.57 kcal/mol. Of all other methods in Table †, only one other method (vDSSB/GAFF2/OPC3/HREM) had a DDG error below 2 kcal/mol. The rest of the methods with predictions for WP6-G4 had DDG errors above an absolute value of 2.66 kcal/mol. The expanded ensemble (EE) methods used GAFF2.11 (OpenFF 2.0.0 parameters were unavailable for WP6-G4 silane group) and predicted binding affinities to be too favorable with errors in the -3 kcal/mol range. The results demonstrate potential force field limitations and the ability of a ML model (based on molecular descriptors) to accurately describe and predict BFEs of silicon-based small molecules like WP6-G4.