One exciting approach to address the challenges associated with synthetic design is computer-aided retrosynthetic planning wherein computational approaches are used to provide synthetic routes. Herein, we report a complementary strategy that combines creative human-generated synthetic plans with robust computational analysis to predict the feasibility of key steps in proposed syntheses. Specifically, we report the development of a neural network model that is used to evaluate human-generated synthetic strategies towards clovane sesquiterpenoids by predicting the yields of key 6-endo-trig radical cyclization steps (Figure ). Our approach uniquely integrates computation into human retrosynthetic analysis through iterative virtual screening.