Our previous work discriminating synthetic cannabinoids using EEMs suggested the potential of discrimination of other drugs that are likely to be fluorescent . A survey of the structures of some the key drugs of abuse (synthetic opioids and benzodiazepines and many others) suggests that fluorescence is likely to be observable in a great many cases owing to the prevalence of a conjugated ring system in many drugs, which then have the potential for relatively bright emission. There are many examples, including our own work with synthetic cannabinoids, which demonstrate that minor substitutions to conjugated ring systems and or to a network of cross-conjugated bonds are sufficient to alter the profile of excitation and emission and in a manner that is characteristic and reproducible for a given solvent. Our data show that this is the case with a range of benzodiazepines, which we use as an example of a large class of structurally diverse illicit drugs, but also extends similarly to opioids. We extend these measurements to include information from reflectance, which we find enables not only enhanced discrimination of the analyte, but also supports concentration discrimination. To establish the generality of this principle we show HSF data for key examples of illicit drugs in Figure . These examples now include four additional opioids that are licensed medications in the UK (7A-D), common recreational 'party drugs' (7E-G; cocaine, MDMA, ketamine) and two NPS not discussed in the main paper [7H, I; a synthetic cathinone (mephedrone) and a psychedelic (2C-B)]. Even to the eye, the HSFs ae simple to discriminate and point to the more general utility of this tool. We remind that these data are collected on a field-portable device and so illustrate the very high potential for these measurements to support community harm reduction activities, including in outreach settings, without the need for specialist device users. Our data illustrate that HSF data are highly discriminatory and can provide both identification and concentration information. The HSF data is highly complex, and we find that an implementation of discrimination by deep learning is effective. Whilst we do not suggest this approach will ever rival lab-based analysis in terms of sensitivity or specificity, we suggest it can be a powerful tool for use by non-experts, in the field. We demonstrate the potential of this through a field-portable prototype, that is small, robust and inexpensive.