Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of synthetically tunable parameters makes it difficult to identify optimal design ``sweet spots'' without rational guiding principles. Here we address this problem combining super-selectivity theory (SST) with analytical models from soft matter and polymer physics into a unified theoretical framework. Starting from an archetypal system, a polymersome functionalized with targeting ligands, we use our model to identify the most selective combination of parameters in terms of particle size, brush polymerization degree and grafting density, as well as tether length, binding affinity and ligands number. We further show how to combine multivalent interactions into multiplexed systems which act holistically as a function of the density of more than one receptor type, so as to achieve binding only when multiple receptors are expressed above a threshold density. We show that theory can be used to effectively fit experimental data and, hence confirming its suitability. We thus propose the design of “bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies.