To meet the demands of modern architectures, optimizing compilers must incorporate an ever larger number of increasingly complex transformation algorithms. Since code transformations may often degrade performance or interfere with subsequent transformations, compilers employ predictive heuristics to guide optimizations by predicting their effects apriori. Unfortunately, the unpredictability of optimization interaction and the irregularity of today?s wide-issue machines severely limit the accuracy of these heuristics. As a result, compiler writers may temper high variance optimizations with overly conservative heuristics or may exclude these optimizations entirely. While this process results in a compiler capable of generating good average code quality across the target benchmark set, it is at the cost of missed optimization opportunities in individual code segments. To replace predictive heuristic, researchers have proposed compilers which explore many optimization options, selecting the best one a posteriori. Unfortunately, these existing iterative compilation techniques are not practical for reasons of compile time and applicability. In this paper, we present the Optimization-Space Exploration (OSE) compiler organization, the first practical iterative compilation strategy applicable to optimizations in general-purpose compilers. Instead of replacing predictive heuristics, OSE uses the compiler writer?s knowledge encoded in the heuristics to select a small number of promising optimization alternatives for a given code segment. Compile time is limited by evaluating only these alternatives for hot code segments using a general compile-time performance estimator. An OSE-enhanced version of Intel?s highly-tuned, aggressively optimizing production compiler for IA-64 yields a significant performance improvement, more than 20% in some cases, on Itanium for SPEC codes.