Directed fuzzing is a critical technique in cybersecurity, targeting specific sections of a program. This approach is essential in various security-related domains such as crash reproduction, patch testing, and vulnerability detection. Despite its importance, current directed fuzzing methods exhibit a trade-off between efficiency and effectiveness. For instance, directed grey-box fuzzing, while efficient in generating fuzzing inputs, lacks sufficient precision. The low precision causes time wasted on executing code that cannot help reach the target site. Conversely, interpreter- or observer-based directed symbolic execution can produce high-quality inputs while incurring non-negligible runtime overhead. These limitations undermine the feasibility of directed fuzzers in real-world scenarios. To kill the birds of efficiency and effectiveness with one stone, in this paper, we involve compilation-based concolic execution into directed fuzzing and present ColorGo, achieving high scalability while preserving the high precision from symbolic execution. ColorGo is a new directed whitebox fuzzer that concretely executes the instrumented program with constraint-solving capability on generated input. It guides the exploration by \textit{incremental coloration}, including static reachability analysis and dynamic feasibility analysis. We evaluated ColorGo on diverse real-world programs and demonstrated that ColorGo outperforms AFLGo by up to \textbf{100x} in reaching target sites and reproducing target crashes.