Problem solving based on compiled associations between elements of the decision space and data is an efficient mode of reasoning for a large percentage of situations faced by an expert. But in some (usually small) percentage of cases, compiled associations are not enough by themselves to lead to correct results. Reasoning from “deeper” levels of understanding offers the advantage of producing correct results even in atypical cases, but at the cost of expanding more computational resources. Thus the trade-off between compiled level systems and deep level systems is between computational efficiency (at the compiled level) and problem-solving generality (at the deep level). We describe a hybrid system containing elements of both deep level reasoning and compiled level reasoning. More particularly, we propose a problem-solving architecture for category-based diagnostic problem solving which at the compiled level centers on classification problem solving and at the deep level uses a type of function-based reasoning. We concentrate in this report on the interaction between the compiled and deep level units and on the mechanisms of function-based reasoning that we employ. We show how our function-based consequence-finding problem solver can be focused by problem solving at the compiled level and how, through such interaction, we obtain the computational efficiency characteristic of compiled level problem solving while retaining the robustness characteristic of deep level problem solving.