Metamodels provide estimates of simulation outputs as a function of design parameters. Often in the design of a system or product, one has performance targets in mind, and would like to identify system design parameters that would yield the target performance vector. Typically this is handled iteratively through an optimization search procedure. As an alternative, one could map system performance requirements to design parameters via an inverse metamodel. Inverse metamodels can be fitted 'for free,' given an experiment design to fit several forward models for multiple performance measures. This paper discusses this strategy, and some of the issues that must be resolved to make the approach practical.