An interactive decision support framework is presented that assists lab researchers in finding optimal product recipes. Within this framework, an approach for sequential experimental design for black box models in a multicriteria optimization context is introduced. An additional criterion involving the prediction error to design new experiments is used with the goal to get a reliable estimate of the Pareto frontier within a few experimental iterations. The resulting decision support approach accompanies the chemist through the whole workflow and supports the user via interactive, graphical elements.