The increasing gap between computational power and I/O performance in new supercomputers drives a shift from an offline approach of data analysis to an inline approach, termed in situ visualization (ISV). While many parallel visualization tools now provide ISV, the trend has been to feed such software with what previously was large dumps of raw data, and let them render everything at the highest possible resolution. This leads to a potentially large performance impact in simulations that support ISV, in particular when ISV is performed interactively. In this paper, we present a smarter method of performing ISV. Our approach aims to detect potentially interesting regions in the generated dataset in order to feed ISV frameworks with only a subset of the data produced by the simulation. While this method mitigates the load on ISV frameworks, making them more efficient and more interactive, it also helps scientists focus on the relevant part of their data. We investigate Smart ISV in the context of a climate simulation, with a set of generic filters derived from information theory, statistics and image processing, and discuss possible trade-offs between performance and quality of data.