Grids became a commonplace for the computation of expensive numerical simulations. This work addresses the problem of searching for relevant simulations and for their results in a grid. This is challenging especially due to the large number of existing simulations, the small semantic differences between them, and the distributed nature of the grid environment. We propose a solution that addresses simultaneously these three challenges by integrating a latent semantic indexing algorithm a linguistic processing module with a grid application framework. This resulted in a novel prototype, ELSIG Explorer, capable of retrieving relevant scenarios computed with Grid SFEA on heterogeneous grids. We evaluated our approach on benchmark datasets from the medical domain and on a set of scenarios for simulating dynamic behavior of biological neural microcircuits in grid.