Abstract In this paper, we consider the problem of noiseless non-adaptive probabilistic group testing, in which the goal is high-probability recovery of the defective set. We show that in the case of $n$ items among which $k$ are defective, the smallest possible number of tests equals $\min \{ C_{k,n} k \log n, n\}$ up to lower-order asymptotic terms, where $C_{k,n}$ is a uniformly bounded constant (varying depending on the scaling of $k$ with respect to $n$) with a simple explicit expression. The algorithmic upper bound follows from a minor adaptation of an existing analysis of the Definite Defectives algorithm, and the algorithm-independent lower bound builds on existing works for the regimes $k \le n^{1-\varOmega (1)}$ and $k = \varTheta (n)$. In sufficiently sparse regimes (including $k = o\big ( \frac{n}{\log n} \big )$), our main result generalizes that of Coja-Oghlan et al. (2020) by avoiding the assumption $k \le n^{1-\varOmega (1)}$, whereas in sufficiently dense regimes (including $k = \omega \big ( \frac{n}{\log n} \big )$), our main result shows that individual testing is asymptotically optimal for any non-zero target success probability, thus strengthening an existing result of Aldridge (2019, IEEE Trans. Inf. Theory, 65, 2058–2061) in terms of both the error probability and the assumed scaling of $k$.