Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model only offers comparisons between two phenotypes at a time: sensitive and resistant. The fitness landscape model with N alleles allows comparisons between N genotypes simultaneously, but does not encode drug dosing data. In clinical settings, there may be a wide, spatio-temporal range of drug concentrations selecting for a variety of adaptive genotypes. There is a need for a more robust model of the pathogen response to therapy to predict resistance and design optimal dosing strategies. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. In this work, we show how N-allele fitness seascapes embed N ∗ 2N unique MSW comparisons. In three clinically relevant pharmacokinetic models, we show how fitness seascapes reveal heterogeneous MSWs, extending the MSW model to more accurately reflect the selection of drug resistant genotypes. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.