Pattern matching algorithms, which may be realized via associative memories, require further improvements in both accuracy and power consumption to achieve more widespread use in real-world applications. In this work we utilized a memristive crossbar to combine computation and memory in an approximate Hamming distance computing architecture for an associative memory. For classifying handwritten digits from the MNIST data-set, we showed that using the Hamming distance rather than the traditional dot product increased accuracy, and decreased power consumption by 100×. Moreover, we showed that we can trade-off accuracy to save additional power or vice-versa by adjusting the input voltage. This trade-off may be adjusted for the architecture depending on its application. Our architecture consumed 200× less power than other previously proposed Hamming distance associative memory architectures, due to the use of memristive devices, and is 256× faster than prior work due to our leveraging of in-memory computation. Improved associative memories should prove useful for GPUs, handwriting recognition, DNA sequence matching, object detection, and other applications.