a Aptamer candidates from a physical pool (white dots) sample a small portion of the fitness landscape (dark gray line), each with a corresponding affinity level (light to dark green). b Particle display discerns the affinity level of each candidate by interrogating the library at multiple stringency levels. c Aptamer sequences and their corresponding affinity levels are used to train and validate a neural network ML model. d The ML model extrapolates new sequences on the fitness landscape in two ways: (1) mutating existing candidates (white dots) in a model-guided fashion (orange dots), and (2) nominating novel sequences in silico, predicting their position on the fitness landscape (white diamonds) and walking top-performing sequences to higher affinity levels (orange diamonds). The extrapolated candidates are synthesized and experimentally tested. e MLPD yields more candidates at each affinity level compared to the initial library, and enables sequence truncation without reduction in affinity.