We consider the approximate support recovery (ASR) task of inferring the support of a $K$-sparse vector ${\bf x} \in \mathbb{R}^n$ from $m$ noisy measurements. We examine the case where $n$ is large, which precludes the application of standard compressed sensing solvers, thereby necessitating solutions with lower complexity. We design a scheme for ASR by leveraging techniques developed for unsourced multiple access. We present two decoding algorithms with computational complexities $\mathcal{O}(K^2 \log n+K \log n \log \log n)$ and $\mathcal{O}(K^3 +K^2 \log n+ K \log n \log \log n)$ per iteration, respectively. When $K \ll n$, this is much lower than the complexity of approximate message passing with a minimum mean squared error denoiser% (AMP-MMSE) ,which requires $\mathcal{O}(mn)$ operations per iteration. This gain comes at a slight performance cost. Our findings suggest that notions from multiple access %such as spreading, matched filter receivers and codes can play an important role in the design of measurement schemes for ASR.