A methodology is presented to solve computer-aided molecular design (CAMD) and process design model problems under consideration of fluid property uncertainty. The uncertainties of the group contribution (GC) property prediction models are quantified for which asymptotic approximation of the covariance of parameter estimation errors is performed following a regression analysis. A Monte Carlo sampling technique generates GC factor samples within the respective uncertainties, which are evaluated separately as constraints to the CAMD optimization problem. The methodology is applied to identify working fluid candidates for an organic Rankine cycle used as waste heat recovery system in a marine diesel engine. CAMD under property uncertainties allows (1) identifying robust and more reliable molecules with respect to property uncertainties (conservative approach) and (2) enhancing the search space in order to find potentially globally optimal working fluids (optimistic approach). Suitable Hydrofluoroolefins (HFO) have been identified as potential working fluids for waste heat recovery. (C) 2018 Elsevier Ltd. All rights reserved.