We present two new methods to search for gravitational waves (GWs) from isolated neutron stars lasting O(hours-days), and the results of two searches of real LIGO/Virgo data. One method is a generalization of a method to detect continuous waves (CWs) that looks for signals that follow a specific power-law model; the other uses machine learning to detect signals that deviate from this model. In both of our searches, we tried to find a remnant of the first-ever binary neutron star (BNS) merger, GW170817, and placed upper limits on the possible emission. For the first method developed, the Generalized FrequencyHough, we present empirical and theoretical estimates of its sensitivity, an extensive follow-up procedure of GW candidates, the parameter space to which it is sensitive, and computational constraints when using it. For the second method, an application of convolutional neural networks, we demonstrate that its sensitivity is comparable to the first method's but is much faster and more robust to signals that do not follow our power-law model. We also characterize the networks in terms of how much data we need to train on to guarantee reasonable efficiencies and false alarm probabilities.
