The generation of large-scale data sets is a fundamental requirement of systems biology. However, despite recent advances, generation of such high-coverage data remains a significant challenge. We developed a novel pooling-deconvolution strategy that can dramatically decrease the effort required. This "PI-Deconvolution" strategy employs imaginary tagging and allows the screening of 2 n probe proteins (baits) in 2*n pools, with n replicates for each bait. Deconvolution of baits with their binding partners (preys) can be achieved by reading the prey's profile from the 2*n experiments. We validated this strategy for protein-protein interaction mapping using both proteome microarrays and a yeast two-hybrid array, demonstrating that PI-Deconvolution can identify interactions accurately with fewer experiments and better coverage. We also show that PI-Deconvolution can identify proteinsmall molecule interactions inferred from profiling the yeast deletion collection. PI-Deconvolution should be applicable to a wide range of library-against-library approaches, and can also be used to optimize array designs.