Protein-protein interactions (PPIs) have evolved to possess binding affinities that best satisfy their functional role. PPIs with different affinities could be structurally very similar, exhibiting similar number and type of intermolecular interactions. To understand how slight structural variations translate into substantial differences in binding affinities, we study binding landscapes, i. e. changes in binding free energy (Gbind) due to all possible mutations. Recently, we developed state-of-the-art methodology that combines protein randomization and affinity sorting coupled to deep sequencing and data normalization that allows us to measure Gbind values for tens of thousands of mutations in a particular PPI. We are using this strategy to map binding landscapes of PPIs with various structures and functions.