Gas-phase chemical plumes exhibit, particularly in the infrared, distinctive emission signatures as a function of wavelength. Hyperspectral imagery can exploit this distinctiveness to detect specific chemicals, even at low concentrations, using matched filters that are tailored both the the specific structure of the chemical signature and to the statistics of the background clutter. But what if the chemical species is unknown? One can apply matched filters to a long list of candidate chemicals (or chemical mixtures), or one can treat the problem as one of anomaly detection. In this case, however, the anomalous signals of interest are not completely unknown. Gas spectra are generically sparse (absorbing or emitting at only a few wavelengths), and this property can be exploited to enhance the sensitivity of anomaly detection algorithms. This paper investigates the utility of sparse signal anomaly detection for the problem of finding plumes of gas with unknown chemistry in hyperspectral imagery.