A substantial body of literature has accumulated on the topic of the estimation of species richness by extrapolation. However, most of these methods rely on an objective sampling of nature. This condition is difficult to meet and seldom achieved for large regions. Furthermore, scientists conducting biological surveys often already have preliminary but subjectively gathered species lists, and would like to assess the completeness of such lists, and/or to find a way to perfect them. We propose several strategies for utilizing external data (such as might be obtained using GIS) to aid in the completion of species lists. These include: (i) using existing species lists to develop predictive models; (ii) using the uniqueness of the environment as a guide to find underrepresented species; (iii) using spectral heterogeneity to locate environmentally heterogeneous regions; (iv) combining surveys with statistical model‐building in an iterative manner. We demonstrate the potential of these approaches using simulation and case studies from Oklahoma.