Model transformation tools implemented using graph transformation techniques are often expected to provide high performance. For this reason, in the Graph Rewriting and Transformation (GReAT) language we have supported two techniques: pre-binding of selected pattern variables and explicit sequencing of transformation steps to improve the performance of the transformation engine. When applied to practical situations, we recognized three shortcomings in our approach: (1) no support for the convenient reuse of results of one rewriting step in another, distant step, (2) lack of a sorting capability for ordering the results of the pattern matching, and (3) absence of support for the distinguished merging of results of multiple pattern matches. In this paper we briefly highlight the relevant features of GReAT, describe three motivating examples that illustrate the problems, introduce our solutions: new extensions to the language, and compare the approaches to other languages.