The fact that MACHINE-LEARNING/NNET/DRAGON-descriptors had the best error metrics (though statistically similar to the next method by ranking) highlights an advantage of using machine learning approaches when sufficient experimental data is available. The other ranked methods for WP6 were approaches relying on MD simulations that would require much more time and computing resources compared to this ML approach, yet the MD based approaches obtained BFEs of similar or worse accuracy. In each of the last three SAMPL iterations, we have received one submission which made use of a ML-based method for predicting BFEs with some success. In this span, these ML approaches have achieved RMSEs of 2.39 kcal/mol or below (1.67 , 2.39 , and 2.04 kcal/mol) for Octa-Acids (SAMPL7 and SAMPL8) and WP6 hosts (SAMPL9), respectively. In the more recent challenges (SAMPL8 and SAMPL9), the ML-based predictions have been among the best performing models as measured by RMSE and MAE error metrics. However, correlation metrics of ML-based models in SAMPL have been inconsistent and relatively poor, with R 2 values of 0.01 , 0.60 , and 0.15 in the present challenge.