The top-n candidates selected via the pareto-search step above form the proposed set from the multi-objective active learner. One can choose from different fronts to progressively create different trade-offs. We could also resort the fronts in terms of an average SCS score or relax uncertainty cut-offs to promote a diverse set of solutions. These criteria are considered by the experts to rank and propose new targets along with complimentary knowledge about the materials that are not codified in the models. The resulting rich set then becomes the focus of attention of the next set of GCMC simulations for following iterations.