Solvent selection is an important step in process synthesis, design, or process modification. The computer-aided molecular design (CAMD) approach, based on the reverse use of group contribution methods, provides a promising tool for solvent selection. However, uncertainties inherent in these techniques and associated models are often neglected. This paper, part 2 of the series, presents a new approach to solvent selection under uncertainty using the Hammersley stochastic annealing (HSTA) algorithm. A real world case study of acetic acid extraction from water, based on two stochastic CAMD models, namely, the infinite dilution activity coefficient model and the solubility parameter model, is presented. This example illustrates the importance of uncertainty in CAMD and demonstrates the usefulness of this HSTA approach to obtain robust decisions.