Nanobodies have recently emerged as alternatives to classical antibodies in therapeutic and diagnostic contexts from parasites to bacteria to viruses, promising improved stability and simpler manufacturing. To improve nanobody discovery efficiency, we developed an integrated experimental and computational pipeline for detailed characterization of the target binding properties of complete alpaca immune repertoires using our custom Nanobody Meta-clustering Analysis Platform (NanoMAP). We tested our pipeline on three distinct pools of targets, immunizing two alpacas with each pool and generating cDNA and phage display libraries from their immune repertoires. We then panned the phage libraries on each target. To produce more detailed binding information, we performed panning variations using subunits, natural variants, intact pathogens, and binding site competitors. Deep sequencing reads from nanobody libraries before and after each panning were pooled and analyzed with NanoMAP to identify nanobody clonal families and assess their levels of enrichment from the library in each panning, reflecting their affinities. NanoMAP outperformed standard clustering methods, producing clonal families that are coherent in sequence and function and detecting rare but high affinity families. By aggregating sequencing data within clonal families, NanoMAP produced reliable and rich data on nanobody repertoire binding phenotypes for each antigen, enhancing nanobody discovery capabilities.