In many settings, innovation involves searching for new combinations in technological spaces defined by mapping efforts. Recent advances in search technologies allow for the collection of large quantities of data to inform exploration decisions in such cases. This method of data-driven recombinant search starkly contrasts with traditional approaches that rely solely on theoretical
understandings to identify breakthroughs. In this paper, I conduct an empirical case study to provide the first investigation of how data can change recombinant search in well-defined landscapes. I study this emergent phenomenon in the context of human genetics, where the advent of genomewide association studies (GWASs) has enabled a form of data-driven search for the genetic roots of diseases divorced from theoretical considerations. By comparing gene-disease combinations
introduced by GWASs with those from theory-driven studies, I provide unique evidence of how
the search process shapes innovation outcomes. My results show that discoveries introduced
by GWASs span a wider portion of the genetic landscape, are more likely to involve neglected
human genes, and are of higher scientific value than comparable combinations introduced by
theory-driven studies. However, heterogeneity analyses reveal that data-driven search performs
poorly with interdependent components because correlational data neglect complex interactions
that only a theoretical understanding can capture. This paper contributes to exploring the boundary
conditions of data-driven search and generates questions for future research on how data shape
innovation.