We explore the practicality of early fault-tolerant quantum algorithms, focusing on ground-state energy estimation problems. Specifically, we address the computation of the cumulative distribution function (CDF) of the spectral measure of the Hamiltonian and the identification of its discontinuities. Scaling to bigger system sizes unveils three challenges: the smoothness of the CDF for large supports, the absence of tight lower bounds on the overlap with the actual ground state, and the complexity of preparing high-quality initial states. To tackle these challenges, we introduce a signal processing technique for identifying the inflection point of the CDF. We argue that this change of paradigm significantly simplifies the problem, making it more accessible while still being accurate. Hence, instead of trying to find the exact ground-state energy, we advocate improving on the classical estimate by aiming at the low-energy support of the initial state. Furthermore, we offer quantitative resource estimates for the maximum number of samples required to identify an increase in the CDF of a given size. Finally, we conduct numerical experiments on a 26-qubit fully-connected Heisenberg model using a truncated density-matrix renormalization group (DMRG) initial state of low bond dimension. Results show that the prediction obtained with the quantum algorithm aligns well with the DMRG-converged energy at large bond dimensions and requires several orders of magnitude fewer samples than predicted by the theory. Hence, we argue that CDF-based quantum algorithms are a viable, practical alternative to quantum phase estimation in resource-limited scenarios.