  A Massey-like inequality is any useful lower bound on guessing entropy in
terms of the computationally scalable Shannon entropy. The asymptotically
optimal Massey-like inequality is determined and further refined for
finite-support distributions. The impact of these results are highlighted for
side-channel attack evaluation where guessing entropy is a key metric. In this
context, the obtained bounds are compared to the state of the art.
