A hybrid experimental/computer-aided methodology for the design of solvents for reactions, recently proposed by the authors [Folic et al., AIChE J. 2007, 53, 1240-1256], is extended. The methodology 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 (CAMD) problem. The treatment of complex reaction Systems, such as competing or consecutive reactions, is considered through the incorporation of a simple reactor model in the problem formulation. This approach is applied to two model reaction schemes, and it is shown that, in principle, it is possible to identify solvents that maximize product formation by enhancing the main reaction and suppressing byproduct formation. Since very few measurements are used to build the reaction model, the effect of uncertainty is tackled explicitly in a stochastic formulation of the CAMD problem. An approach to sensitivity analysis for the identification of the key model parameters is discussed. Using this information to generate scenarios, a stochastic optimization problem (whose objective is to determine the solvents with the best expected performance) is then solved. The final output consists of a list of candidate solvents that can be targeted for experimentation. The methodology is demonstrated on a Menschutkin reaction, which is a representative S(N)2 reaction. This shows that the uncertainty in the reaction model has little impact on the types of solvent molecules that have the best performance. Dinitrates are found to be a promising class of solvents, with regard to maximizing the reaction rate constant.