Prospects to assess and explore largely untapped plant genetic resources (PGR) collections to search for climate change related traits, such as drought and heat tolerance, as well as pest and disease resistance, are possible through new approaches such as the focused identification of germplasm strategy (FIGS). FIGS approach is based on the paradigm that any germplasm is likely to reflect the selection pressures of the environment under which it evolved. The approach uses trait and environmental data (climate data including phenology data) to develop a priori information based on the quantification of the trait-environment relationship. If a dependency between the trait and the environment is detected, the a priori information is then used to define subsets of accessions with a high probability of containing the sought after traits. The subsets of accessions are then used for a posteriori evaluation. Recent research comparing a priori and a posteriori information supports the assertion that FIGS can be used as an effective tool to search for traits of resistance to pests and diseases as well as traits to adapt to climate change. This paper presents and discusses some of the recent results where FIGS was used to develop subsets with high probability of finding desirable traits, such as resistance to stripe (yellow) rust, in durum wheat. It also addresses ways in which current FIGS based models could be further enhanced by working the ways in which the environmental data is presented to the models, thereby improving the detection of traits associated with climate change adaptation.