Cocaine is one of the most widely used illicit drugs worldwide. Cocaine powders seized by the Police may contain numerous other substances besides the drug itself. These can be impurities originating from the coca plant or the production process, or be purposely added to the drug formulation as adulterants and cutting agents. In forensic laboratories, identification of cocaine is routinely done through GC-MS analysis, but other components are often ignored even if the method allows for their detection. Yet, they can provide valuable insight into the history of a seizure and its potential connection to other samples. To explore this idea, an extensive review of common impurities and adulterants encountered in cocaine is presented. Based on their incidence, concentration in the end product and compatibility with GC-MS methods, their overall usefulness as candidates for the statistical investigation of existing forensic data is evaluated. The impurities cis- and trans-cinnamoylcocaine, tropacocaine, norcocaine and N-benzoylnormethylecgonine as well as the adulterants lidocaine, procaine, tetracaine, benzocaine, caffeine, acetylsalicylic acid, phenacetin, ibuprofen, levamisole, hydroxyzine and diltiazem are promising candidates to provide additional forensic intelligence. Future research on optimized routine GC-MS methods, signal reproducibility, comparison, statistics and databases is suggested to facilitate this concept. Ultimately, such an approach may significantly advance the amount of information that is extracted from routine casework data, elucidate developments in the cocaine markets in the past and facilitate Police work in the future. Preliminary assessment of existing data from the forensic laboratory of the Amsterdam Police has been included to show that the detection of the identified target impurities is feasible, and that small adjustments to the analysis method could significantly increase the detectability of these analytes in prospective drug screenings. Forensic intelligence based on retrospective data mining of cocaine containing casework samples may thus be realized with minimal additional laboratory efforts by using already available instrumentation, samples and data.