We find that predictive accuracy of binding free energies for host-guests, in terms of RMSE, is comparable to previous SAMPL challenges. However, we do see moderate improvement in some cases. For instance, binding affinity predictions of hosts in the acyclic cucurbituril category such as H1 featured in SAMPL3 , CBClip from SAMPL5 , and TrimerTrip (SAMPL7) had a mean RMSE of 7.07, 5.87, and 4.15 kcal/mol, respectively. The best performing methods for acyclic cucurbiturils achieved RMSEs as low as 1.60, 3.40, and 1.58 kcal/mol. The accuracy of methods used for acyclic cucurbiturls similar to TrimerTrip show improvement across SAMPL challenges. On the other hand, methods used in predicting binding free energies for systems in the cavitand category OA/TEMOA (SAMPL5), OA/TEMOA (SAMPL6), and OA/exoOA (SAMPL7) show high variation from challenge to challenge. The RMSE across challenges shows similar or slightly poorer accuracy on average. However, the best performing methods in the cavitand category usually do better than methods in other categories, or at least as well, by RMSE, and achieve R 2 values well above 0.7 kcal/mol. This is more apparent in SAMPL6 and SAMPL7, partly from methods using the extensive cavitand data available from previous challenges to apply corrections. Comparing accuracy of ranked and non-ranked methods, on average ranked methods performed better (Figure ). In addition, we find participation in the SAMPL host-guest challenges to be fairly consistent over time with approximately 30 submissions (the exact submission amount shown in parenthesis next to the SAMPL challenge) each in SAMPL3 (29), SAMPL5 , and SAMPL7 , except the substantial increase to 80 submissions for SAMPL6.