We find the predictive accuracy of less computationally expensive models (machine learning and docking) was better or at least similar to more expensive and complex simulation based methods on the WP6 dataset. Using a ML model with molecular descriptors or an empirical approach from AutoDock Vina's smina scoring function for host-guest systems has a speed and perhaps cost effective advantage for BFE accuracy. ML based models have been among top performing methods (based on predictive accuracy) in recent SAMPL iterations, with improvement in correlation metrics for Octa Acid hosts (from SAMPL7-8), but otherwise correlation metrics have been inconsistent in other host families (CB8 and WP6) and typically among the lowest. In contrast, simulation based approaches had much better correlation to WP6 experimental measurements. Force field-based methods also show particularly severe errors for some chemistry, e.g. a sulfur-containing guest was among the worst performers and larger errors were obtained for a silicon-containing guest (discussed in section 3.2). The combined strengths of these approaches -low errors with ML approaches but good correlation with force field based approaches -make a hybrid approach interesting. Some work in this direction has already been done; for example, some recent approaches have combined RBFEs (relative binding free energy calculations) and ML with success. Further work in this direction may thus be interesting for host-guest systems in SAMPL.