diff --git a/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl b/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl
index 1ba8456d2..dcf187d45 100644
--- a/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl
+++ b/lib/OptimizationOptimJL/src/OptimizationOptimJL.jl
@@ -95,7 +95,7 @@ function SciMLBase.__init(prob::OptimizationProblem,
                 if prob.f isa OptimizationFunction && (!(prob.f.adtype isa SciMLBase.NoAD) || !isnothing(prob.f.grad))
                     opt = Optim.Fminbox(opt)
                 else
-                    throw(ArgumentError("Fminbox($opt) requires gradients, since you didn't use `OptimizationFunction` with a valid AD backend https://docs.sciml.ai/Optimization/stable/API/ad/ the lower and upper bounds thus will be ignored."))
+                    throw(ArgumentError("Fminbox($opt) requires gradients, use `OptimizationFunction` either with a valid AD backend https://docs.sciml.ai/Optimization/stable/API/ad/ or a provided 'grad' function."))
                 end
             end
         end
diff --git a/lib/OptimizationOptimJL/test/runtests.jl b/lib/OptimizationOptimJL/test/runtests.jl
index 138519700..4dc4aac66 100644
--- a/lib/OptimizationOptimJL/test/runtests.jl
+++ b/lib/OptimizationOptimJL/test/runtests.jl
@@ -174,6 +174,30 @@ end
     sol = solve(prob, BFGS())
     @test 10 * sol.objective < l1
       
+    function rosenbrock_grad!(dx, x, p)
+        dx[1] = -2*(p[1] - x[1]) -4 * p[2] * (x[2] - x[1]^2)*x[1]
+        dx[2]= 2*p[2]*(x[2]-x[1]^2)
+        return nothing
+    end
+
+    # https://github.com/SciML/Optimization.jl/issues/754 Optim.BFGS() with explicit gradient function
+    optprob = OptimizationFunction(rosenbrock; grad=rosenbrock_grad!)
+    prob = OptimizationProblem(optprob, x0, _p)
+    @test (sol = solve(prob, Optim.BFGS())) isa Any # test exception not thrown
+    @test 10 * sol.objective < l1
+
+    # https://github.com/SciML/Optimization.jl/issues/754 Optim.BFGS() with bounds and explicit gradient function
+    optprob = OptimizationFunction(rosenbrock; grad=rosenbrock_grad!)
+    prob = OptimizationProblem(optprob, x0, _p;  lb = [-1.0, -1.0], ub = [0.8, 0.8])
+    @test (sol = solve(prob, Optim.BFGS())) isa Any  # test exception not thrown
+    @test 10 * sol.objective < l1
+
+    # test that Optim.BFGS() with bounds but no AD or user-supplied gradient fails
+    optprob = OptimizationFunction(rosenbrock, SciMLBase.NoAD())
+    prob = OptimizationProblem(optprob, x0, _p;  lb = [-1.0, -1.0], ub = [0.8, 0.8])
+    @test_throws ArgumentError (sol = solve(prob, Optim.BFGS())) isa Any  # test exception is thrown
+    @test 10 * sol.objective < l1
+    
     @testset "cache" begin
         objective(x, p) = (p[1] - x[1])^2
         x0 = zeros(1)