Users often have to search for a most preferred item but do not know how to state their preferences in the language allowed by the system. Example-Critiquing has been proposed as a mixed-initiative technique for allowing them to construct their preference model in an effective way. In this technique, users volunteer their preferences as critiques on examples. It is thus important to stimulate their preference expression by the proper choice of examples, called suggestions. We analyze what suggestions should be and derive several new techniques for computing them. We prove their effectiveness using simulations and live user studies. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.