In this paper, we revisit an efficient algorithm for noisy group testing in which each item is decoded separately (Malyutov and Mateev, 1980), and develop novel performance guarantees via an information-theoretic framework for general noise models. For the noiseless and symmetric noise models, we find that the asymptotic number of tests required for vanishing error probability is within a factor log 2 ≈ 0.7 of the informationtheoretic optimum at low parsity levels, and that when a small fraction of incorrectly-decoded items is allowed, this guarantee extends to all sublinear sparsity levels. In many scaling regimes, these are the best known theoretical guarantees for any noisy group testing algorithm.