Cellular receptor systems are expected to present complex ligand interaction patterns that cannot be evaluated assuming a simple one ligand:one receptor interaction model. We have previously evaluated heterogeneous interactions using an alternative method to regression analysis, called Interaction Map (IM). IM decomposes a time-resolved binding curve into its separate components. By replacing the reductionistic, scalar kinetic association rate constant k(a) and dissociation rate constant k(d) with a two-dimensional distribution of k(a) and k(d), it is possible to display heterogeneous data as a map where each peak corresponds to one of the components that contribute to the cumulative binding curve. Here we challenge the Interaction Map approach by artificially generating heterogeneous data from two known interactions, on either LigandTracer or Surface Plasmon Resonance devices. We prove the ability of IM to accurately decompose these man-made heterogeneous binding curves composed of two different interactions. We conclude that the Interaction Map approach is well suited for the analysis of complex binding data and forecast that it has a potential to resolve previously uninterpretable data, in particular those generated in cell-based assays.