We have developed a sequential set of computational screens that may prove useful for evaluating analyte sets for their ability to accurately report on metabolic fluxes. The methodology is problem-centric in that the screens are used in the context of a particular metabolic engineering problem. That is, flux bounds and alternative flux routings are first identified for a particular problem, and then the information is used to inform the design of nuclear magnetic resonance (NMR) experiments. After obtaining the flux bounds via MILP, analytes are first screened for whether the predicted NMR spectra associated with various analytes can differentiate between different extreme point (or linear combinations of extreme point) flux solutions. The second screen entails determining whether the analytes provide unique flux values or multiple flux solutions. Finally, the economics associated with using different analytes is considered in order to further refine the analyte selection process in terms of an overall utility index, where the index summarizes the cost-benefit attributes by quantifying benefit (contrast power) per cost (e.g., NMR instrument time required). We also demonstrate the use of an alternative strategy, the Analytical Hierarchy Process, for ranking analytes based on the individual experimentalist's-generated weights assigned for the relative value of flux scenario contrast, unique inversion of NMR data to fluxes, etc.