Members of the oomycete genus Phytophthora cause some of the most devastating plant diseases in the world and are arguably the most destructive pathogens of dicot plants. Phytophthora research has entered the genomics era. Current genomic resources include expressed sequence tags from a variety of developmental and infection stages, as well as sequences of selected regions of Phytophthora genomes. Genomics promise to impact upon our understanding of the molecular basis of infection by Phytophthora, for example, by facilitating the isolation of genes encoding effector molecules with a role in virulence and avirulence. Based on prevalent models of plant–pathogen coevolution, some of these effectors, notably those with avirulence functions, are predicted to exhibit significant sequence variation within populations of the pathogen. This and other features were used to identify candidate avirulence genes from sequence databases. Here, we describe a strategy that combines data mining with intraspecific comparative genomics and functional analyses for the identification of novel avirulence genes from Phytophthora. This approach provides a rapid and efficient alternative to classical positional cloning strategies for identifying avirulence genes that match known resistance genes. In addition, this approach has the potential to uncover ‘orphan’ avirulence genes for which corresponding resistance genes have not previously been characterized.