A hybrid experimental/computer-aided methodology for the design of solvents for reactions is presented. It is based on the use of a few reaction rate measurements to build a reaction model, followed by the formulation and solution of an optimal computer-aided molecular design problem (CAMD) in which the reaction rate under given conditions is maximized. In order to verily the suitability of the solvent candidates identified in the CAMD step, and to assess the reliability of the model used, feedback can be introduced. When the reliability of the model is found to be insufficient, experimental rate data for the candidate solvents are obtained and added to the original data set to create an updated reaction model, which can be used to find new candidate solvents. Since very few measurements are used to build the reaction model, we perform a sensitivity analysis on the model to assess the impact of uncertainty. Using this information to generate scenarios, we then solve a stochastic optimization problem, which aims to determine the solvents that give the best average performance. The final output consists of a list of candidate solvents which can be targeted for experimentation. This methodology is illustrated, step by step, through application to a solvolysis reaction. (c) 2007 American Institute of Chemical Engineers.