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Even though reinforcement-learning-based algorithms achieved superhuman performance in many domains, the field of robotics poses significant challenges as the state and action spaces are continuous, and the reward function is predominantly sparse. In this work, we propose: 1) HiER: highlight experience replay that creates a secondary replay buffer for the most relevant experiences, 2) E2H-ISE: an easy2hard data collection curriculum-learning method based on controlling the entropy of the initial state-goal distribution and with it, indirectly, the task difficulty, and 3) HiER+: the combination of HiER and E2H-ISE. They can be applied with or without the techniques of hindsight experience replay (HER) and prioritized experience replay (PER). While both HiER and E2H-ISE surpass the baselines, HiER+ further improves the results and significantly outperforms the state-of-the-art on the push, slide, and pick-and-place robotic manipulation tasks. Our implementation and further media materials are available on the project site.