The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have introspective access to that knowledge, their explanations of actual search considerations seems very valuable in constructing a knowledge level model of their search processes. The incremental method was inspired by the work on Ripple-Down Rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. We substantially extend Ripple Down Rules to allow undefined terms in the conditions. These undefined terms in turn become defined by Ripple Down Rules. The resulting framework is called Nested Ripple Down Rules. Our system SmS1.2 (SmS for Smart Searcher), has been employed for the acquisition of expert chess knowledge for performing a highly pruned tree search. Our first experimental results in the chess domain are evidence for the validity of our approach, even though a number of the planned features are still under development.