We consider example-critiquing systems that help people search for their most preferred item in a large catalog. We first analyze how such systems can help users in the framework of three existing example-critiquing approaches (RABBIT, Incremental-critiquing and ATA). Afterwards we consider the use of several novel types of explicit passive analysis to guide the users in their search when their original query returns no hits. We suggest that a user-centric search system together with the right explicit passive analysis makes the users feel more confident in their decision and reduces session time and cognitive effort. Finally we present the results of a pilot study.