We believe predictions are only valuable when they include an accurate assessment of uncertainty, otherwise downstream users have no guidance as to the reliability of such predictions and thus no confidence as to when they can usefully be used and when they should be ignored. These uncertainties are even more valuable if they are determined based on the input molecule, capturing when reliability varies with chemistry. Unfortunately, ten out of 34 type III submissions in SAMPL6 provided no uncertainties with their predictions. Perhaps requiring such predictions for every submission would improve future challenges and drive progress in this respect. From the beginning, we considered providing an uncertainty evaluation for each prediction an important component of our model. Thus, our ability to determine accurate uncertainty predictions based on input chemistry shows our model's potential to be a successful predictive method. Previous SAMPL challenges have highlighted the importance and difficulty in accurately assessing model uncertainty for hydration free energies and distribution coefficients . The large error bars for ionizable sites we consider outside our domain of applicability provide evidence our uncertainty estimates are working as desired. QQ plots also support the conclusion that our model is capable of predicting its own uncertainty (Figure ). SAMPL6 was an opportunity to test our Gaussian process model on an external test set and our first completely blind set of predictions. Our new Gaussian process model performed semi-competitively, especially considering its limited training set compared to more established methods which participated. We look forward to incorporating important lessons from this challenge, particularly, expanding our definition of an ionizable group and improving our likelihood function to include polyprotic molecules in our next training set. Overall, SAMPL challenges provide an important service to the community allowing participants to test their predictive models in a blind manner.